Publications by authors named "Gisbert Schneider"

286 Publications

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment.

J Chem Inf Model 2021 Feb 25. Epub 2021 Feb 25.

Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, 8049 Zurich, Switzerland.

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and molecule generation. However, these models are considered "black-box" and "hard-to-debug". This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, hERG channel inhibition, passive permeability, and cytochrome P450 inhibition. The proposed methodology highlighted molecular features and structural elements that are in agreement with known pharmacophore motifs, correctly identified property cliffs, and provided insights into unspecific ligand-target interactions. The developed XAI approach is fully open-sourced and can be used by practitioners to train new models on other clinically relevant endpoints.
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http://dx.doi.org/10.1021/acs.jcim.0c01344DOI Listing
February 2021

Virtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors.

Mol Inform 2020 09 9;39(9):e2000109. Epub 2020 Jul 9.

Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.

Ligand-based virtual screening of large compound collections, combined with fast bioactivity determination, facilitate the discovery of bioactive molecules with desired properties. Here, chemical similarity based machine learning and label-free differential scanning fluorimetry were used to rapidly identify new ligands of the anticancer target Pim-1 kinase. The three-dimensional crystal structure complex of human Pim-1 with ligand bound revealed an ATP-competitive binding mode. Generative de novo design with a recurrent neural network additionally suggested innovative molecular scaffolds. Results corroborate the validity of the chemical similarity principle for rapid ligand prototyping, suggesting the complementarity of similarity-based and generative computational approaches.
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http://dx.doi.org/10.1002/minf.202000109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539333PMC
September 2020

Engineering of a functional γ-tocopherol transfer protein.

Redox Biol 2021 Jan 4;38:101773. Epub 2020 Nov 4.

University of Bern, Department of Chemistry and Biochemistry, Bern, 3012, Switzerland. Electronic address:

α-tocopherol transfer protein (TTP) was previously reported to self-aggregate into 24-meric spheres (α-TTP) and to possess transcytotic potency across mono-layers of human umbilical vein endothelial cells (HUVECs). In this work, we describe the characterisation of a functional TTP variant with its vitamer selectivity shifted towards γ-tocopherol. The shift was obtained by introducing an alanine to leucine substitution into the substrate-binding pocket at position 156 through site directed mutagenesis. We report here the X-ray crystal structure of the γ-tocopherol specific particle (γ-TTP) at 2.24 Å resolution. γ-TTP features full functionality compared to its α-tocopherol specific parent including self-aggregation potency and transcytotic activity in trans-well experiments using primary HUVEC cells. The impact of the A156L mutation on TTP function is quantified in vitro by measuring the affinity towards γ-tocopherol through micro-differential scanning calorimetry and by determining its ligand-transfer activity. Finally, cell culture experiments using adherently grown HUVEC cells indicate that the protomers of γ-TTP, in contrast to α-TTP, do not counteract cytokine-mediated inflammation at a transcriptional level. Our results suggest that the A156L substitution in TTP is fully functional and has the potential to pave the way for further experiments towards the understanding of α-tocopherol homeostasis in humans.
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http://dx.doi.org/10.1016/j.redox.2020.101773DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677715PMC
January 2021

Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides.

Biochemistry 2020 Oct 16;59(39):3772-3781. Epub 2020 Sep 16.

Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.

Naturally occurring membranolytic antimicrobial peptides (AMPs) are rarely cell-type selective and highly potent at the same time. Template-based peptide design can be used to generate AMPs with improved properties . Following this approach, 18 linear peptides were obtained by computationally morphing the natural AMP Aurein 2.2d2 GLFDIVKKVVGALG into the synthetic model AMP KLLKLLKKLLKLLK. Eleven of the 18 chimeric designs inhibited the growth of , and six peptides were tested and found to be active against one resistant pathogenic strain or more. One of the peptides was broadly active against bacterial and fungal pathogens without exhibiting toxicity to certain human cell lines. Solution nuclear magnetic resonance and molecular dynamics simulation suggested an oblique-oriented membrane insertion mechanism of this helical peptide. Temperature-resolved circular dichroism spectroscopy pointed to conformational flexibility as an essential feature of cell-type selective AMPs.
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http://dx.doi.org/10.1021/acs.biochem.0c00565DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7547863PMC
October 2020

A novel FRET peptide assay reveals efficient Helicobacter pylori HtrA inhibition through zinc and copper binding.

Sci Rep 2020 06 29;10(1):10563. Epub 2020 Jun 29.

Microbiology, Department of Biosciences, University of Salzburg, Billrothstrasse 11, 5020, Salzburg, Austria.

Helicobacter pylori (H. pylori) secretes the chaperone and serine protease high temperature requirement A (HtrA) that cleaves gastric epithelial cell surface proteins to disrupt the epithelial integrity and barrier function. First inhibitory lead structures have demonstrated the essential role of HtrA in H. pylori physiology and pathogenesis. Comprehensive drug discovery techniques allowing high-throughput screening are now required to develop effective compounds. Here, we designed a novel fluorescence resonance energy transfer (FRET) peptide derived from a gel-based label-free proteomic approach (direct in-gel profiling of protease specificity) as a valuable substrate for H. pylori HtrA. Since serine proteases are often sensitive to metal ions, we investigated the influence of different divalent ions on the activity of HtrA. We identified Zn and Cu ions as inhibitors of H. pylori HtrA activity, as monitored by in vitro cleavage experiments using casein or E-cadherin as substrates and in the FRET peptide assay. Putative binding sites for Zn and Cu were then analyzed in thermal shift and microscale thermophoresis assays. The findings of this study will contribute to the development of novel metal ion-dependent protease inhibitors, which might help to fight bacterial infections.
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http://dx.doi.org/10.1038/s41598-020-67578-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324608PMC
June 2020

Filovirus Antiviral Activity of Cationic Amphiphilic Drugs Is Associated with Lipophilicity and Ability To Induce Phospholipidosis.

Antimicrob Agents Chemother 2020 07 22;64(8). Epub 2020 Jul 22.

Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany

Several cationic amphiphilic drugs (CADs) have been found to inhibit cell entry of filoviruses and other enveloped viruses. Structurally unrelated CADs may have antiviral activity, yet the underlying common mechanism and structure-activity relationship are incompletely understood. We aimed to understand how widespread antiviral activity is among CADs and which structural and physico-chemical properties are linked to entry inhibition. We measured inhibition of Marburg virus pseudoparticle (MARVpp) cell entry by 45 heterogeneous and mostly FDA-approved CADs and cytotoxicity in EA.hy926 cells. We analyzed correlation of antiviral activity with four chemical properties: pKa, hydrophobicity (octanol/water partitioning coefficient; ClogP), molecular weight, and distance between the basic group and hydrophobic ring structures. Additionally, we quantified drug-induced phospholipidosis (DIPL) of a CAD subset by flow cytometry. Structurally similar compounds (derivatives) and those with similar chemical properties but unrelated structures (analogues) to those of strong inhibitors were obtained by two similarity search approaches and tested for antiviral activity. Overall, 11 out of 45 (24%) CADs inhibited MARVpp by 40% or more. The strongest antiviral compounds were dronedarone, triparanol, and quinacrine. Structure-activity relationship studies revealed highly significant correlations between antiviral activity, hydrophobicity (ClogP > 4), and DIPL. Moreover, pKa and intramolecular distance between hydrophobic and hydrophilic moieties correlated with antiviral activity but to a lesser extent. We also showed that in contrast to analogues, derivatives had antiviral activity similar to that of the seed compound dronedarone. Overall, one-quarter of CADs inhibit MARVpp entry , and antiviral activity of CADs mostly relies on their hydrophobicity yet is promoted by the individual structure.
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http://dx.doi.org/10.1128/AAC.00143-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526846PMC
July 2020

Interaction analysis of glycoengineered antibodies with CD16a: a native mass spectrometry approach.

MAbs 2020 Jan-Dec;12(1):1736975

Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland.

Minor changes in the quality of biologically manufactured monoclonal antibodies (mAbs) can affect their bioactivity and efficacy. One of the most important variations concerns the N-glycosylation pattern, which directly affects an anti-tumor mechanism called antibody-dependent cell-meditated cytotoxicity (ADCC). Thus, careful engineering of mAbs is expected to enhance both protein-receptor binding and ADCC. The specific aim of this study is to evaluate the influence of terminal carbohydrates within the Fc region on the interaction with the FcγRIIIa/CD16a receptor in native and label-free conditions. The single mAb molecule comprises variants with minimal and maximal galactosylation, as well as α2,3 and α2,6-sialic acid isomers. Here, we apply native electrospray ionization mass spectrometry to determine the solution-phase antibody-receptor equilibria and by using temperature-controlled nanoelectrospray, a thermal stability of the complex is examined. Based on these, we prove that the galactosylation of a fucosylated Fc region increases the binding to CD16a 1.5-fold when compared with the non-galactosylated variant. The α2,6-sialylation has no significant effect on the binding, whereas the α2,3-sialylation decreases it 1.72-fold. In line with expectation, the galactoslylated and α2,6-sialylated mAb:CD16a complex exhibit higher thermal stability when measured in the temperature gradient from 20 to 50°C. The similar binding pattern is observed based on surface plasmon resonance analysis and immunofluorescence staining using natural killer cells. The results of our study provide new insight into N-glycosylation-based interaction of the mAb:CD16a complex.
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http://dx.doi.org/10.1080/19420862.2020.1736975DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153833PMC
February 2021

Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (-)-Englerin A Mimetics.

ChemMedChem 2020 04 12;15(7):566-570. Epub 2020 Mar 12.

Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.

Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (-)-englerin A. Two of the top-ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (-)-englerin A. Intracellular calcium assays and electrophysiological whole-cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer-generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape-based molecular representation for molecular scaffold-hopping.
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http://dx.doi.org/10.1002/cmdc.202000017DOI Listing
April 2020

Bidirectional Molecule Generation with Recurrent Neural Networks.

J Chem Inf Model 2020 03 16;60(3):1175-1183. Epub 2020 Jan 16.

Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland.

Recurrent neural networks (RNNs) are able to generate de novo molecular designs using simplified molecular input line entry systems (SMILES) string representations of the chemical structure. RNN-based structure generation is usually performed unidirectionally, by growing SMILES strings from left to right. However, there is no natural start or end of a small molecule, and SMILES strings are intrinsically nonunivocal representations of molecular graphs. These properties motivate bidirectional structure generation. Here, bidirectional generative RNNs for SMILES-based molecule design are introduced. To this end, two established bidirectional methods were implemented, and a new method for SMILES string generation and data augmentation is introduced-the bidirectional molecule design by alternate learning (BIMODAL). These three bidirectional strategies were compared to the unidirectional forward RNN approach for SMILES string generation, in terms of the (i) novelty, (ii) scaffold diversity, and (iii) chemical-biological relevance of the computer-generated molecules. The results positively advocate bidirectional strategies for SMILES-based molecular de novo design, with BIMODAL showing superior results to the unidirectional forward RNN for most of the criteria in the tested conditions. The code of the methods and the pretrained models can be found at URL https://github.com/ETHmodlab/BIMODAL.
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http://dx.doi.org/10.1021/acs.jcim.9b00943DOI Listing
March 2020

Molecular Design with Generative Long Short-term Memory.

Chimia (Aarau) 2019 Dec;73(12):1006-1011

ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland;, Email:

Drug discovery benefits from computational models aiding the identification of new chemical matter with bespoke properties. The field of drug design has been particularly revitalized by adaptation of generative machine learning models from the field of natural language processing. These deep neural network models are trained on recognizing molecular structures and generate new molecular entities without relying on pre-determined sets of molecular building blocks and chemical transformations for virtual molecule construction. Implicit representation of chemical knowledge provides an alternative to formulating the molecular design task in terms of the established, explicit chemical vocabulary. Here, we review molecular design approaches from the field of 'artificial intelligence', focusing on instances of deep generative models, and highlight the prospective application of long short-term memory models to hit and lead finding in medicinal chemistry.
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http://dx.doi.org/10.2533/chimia.2019.1006DOI Listing
December 2019

Rethinking drug design in the artificial intelligence era.

Nat Rev Drug Discov 2020 05 4;19(5):353-364. Epub 2019 Dec 4.

ETH Zurich, RETHINK, Department of Chemistry and Applied Biosciences, Zurich, Switzerland.

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.
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http://dx.doi.org/10.1038/s41573-019-0050-3DOI Listing
May 2020

Structural insights into the interaction of botulinum neurotoxin a with its neuronal receptor SV2C.

Toxicon 2020 Feb 26;175:36-43. Epub 2019 Nov 26.

Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland. Electronic address:

A dual-receptor interaction with a polysialoganglioside and synaptic vesicle glycoprotein 2 (SV2) is required for botulinum neurotoxin A (BoNT) toxicity. Here, we review what is currently known about the BoNT/A-SV2 interaction based on structural studies. Currently, five crystal structures of the receptor-binding domain (Hc) of BoNT subtypes A1 and A2 complexed to the large luminal domain (LD4) of SV2C have been determined. On the basis of the available structures, we will discuss the importance of protein-protein and protein-carbohydrate interactions for BoNT/A toxicity as well as the high plasticity of BoNT/A for receptor recognition by tolerating a variety of side-chain interactions at the interface. A plausible explanation how receptor-binding specificity of BoNT/A may be achieved without an extensive and conserved side chain-side chain interaction network will be provided.
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http://dx.doi.org/10.1016/j.toxicon.2019.11.010DOI Listing
February 2020

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning.

ChemistryOpen 2019 Oct 23;8(10):1303-1308. Epub 2019 Oct 23.

ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 CH-8093 Zurich Switzerland.

Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell-migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top-scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.
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http://dx.doi.org/10.1002/open.201900222DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6807213PMC
October 2019

Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies.

J Cheminform 2019 Sep 11;11(1):59. Epub 2019 Sep 11.

Bayer AG, Pharmaceuticals, R&D, 42096, Wuppertal, Germany.

We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our acceptor and donor databases are the largest on record with 4426 and 1036 data points, respectively. After scanning over radial atomic descriptors and ML methods, our final trained HBA and HBD ML models achieve RMSEs of 3.8 kJ mol (acceptors), and 2.3 kJ mol (donors) on experimental test sets, respectively. This performance is comparable with previous models that are trained on experimental hydrogen bonding free energies, indicating that molecular QC data can serve as substitute for experiment. The potential ramifications thereof could lead to a full replacement of wetlab chemistry for HBA/HBD strength determination by QC. As a possible chemical application of our ML models, we highlight our predicted HBA and HBD strengths as possible descriptors in two case studies on trends in intramolecular hydrogen bonding.
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http://dx.doi.org/10.1186/s13321-019-0381-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737620PMC
September 2019

Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning.

Chembiochem 2020 02 14;21(4):500-507. Epub 2019 Nov 14.

Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, RETHINK, Vladimir-Prelog-Weg 4, 8093, Zürich, Switzerland.

Deep convolutional neural networks (CNNs) are a method of choice for image recognition. Herein a hybrid CNN approach is presented for molecular pattern recognition in drug discovery. Using self-organizing map images of molecular pharmacophores as input, CNN models were trained to identify chemokine receptor CXCR4 modulators with high accuracy. This machine learning classifier identified first-in-class synthetic CXCR4 full agonists. The receptor-activating effects were confirmed by intracellular cAMP response and in a phenotypic spheroid invasion assay of medulloblastoma cell invasion. Additional macromolecular targets of the small molecules were predicted in silico and tested in vitro, revealing modulatory effects on dopamine receptors and CCR1. These results positively advocate the applicability of molecular image recognition by CNNs to ligand-based virtual compound screening, and demonstrate the complementarity of machine intelligence and human expert knowledge.
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http://dx.doi.org/10.1002/cbic.201900346DOI Listing
February 2020

In silico design and optimization of selective membranolytic anticancer peptides.

Sci Rep 2019 08 2;9(1):11282. Epub 2019 Aug 2.

Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland.

Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.
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http://dx.doi.org/10.1038/s41598-019-47568-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677754PMC
August 2019

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery.

Chem Rev 2019 09 11;119(18):10520-10594. Epub 2019 Jul 11.

State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China.

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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http://dx.doi.org/10.1021/acs.chemrev.8b00728DOI Listing
September 2019

Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning.

ChemMedChem 2019 06 16;14(12):1129-1134. Epub 2019 May 16.

Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.

A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targets of (-)-galantamine, with different polypharmacological profiles. Two of the computer-generated hits possess an expanded spectrum of bioactivity on targets relevant to the treatment of Alzheimer's disease and are suitable for hit-to-lead expansion. These results advocate multitarget drug design by advanced virtual screening protocols based on chemically informed machine learning models.
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http://dx.doi.org/10.1002/cmdc.201900097DOI Listing
June 2019

De novo design of anticancer peptides by ensemble artificial neural networks.

J Mol Model 2019 Apr 5;25(5):112. Epub 2019 Apr 5.

Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.

Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.
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http://dx.doi.org/10.1007/s00894-019-4007-6DOI Listing
April 2019

Identification of Chemokine Ligands by Biochemical Fragmentation and Simulated Peptide Evolution.

Angew Chem Int Ed Engl 2019 05 15;58(21):7138-7142. Epub 2019 Apr 15.

Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.

Short linear peptides can overcome certain limitations of small molecules for targeting protein-protein interactions (PPIs). Herein, the interaction between the human chemokine CCL19 with chemokine receptor CCR7 was investigated to obtain receptor-derived CCL19-binding peptides. After identifying a linear binding site of CCR7, five hexapeptides binding to CCL19 in the low micromolar to nanomolar range were designed, guided by pharmacophore and lipophilicity screening of computationally generated peptide libraries. The results corroborate the applicability of the computational approach and the chosen selection criteria to obtain short linear peptides mimicking a protein-protein interaction site.
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http://dx.doi.org/10.1002/anie.201902022DOI Listing
May 2019

Automated De Novo Drug Design: Are We Nearly There Yet?

Angew Chem Int Ed Engl 2019 08 17;58(32):10792-10803. Epub 2019 May 17.

Charles River, 6-9 Spire Green Centre, Harlow, Essex, CM19 5TR, UK.

Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.
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http://dx.doi.org/10.1002/anie.201814681DOI Listing
August 2019

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning.

ChemistryOpen 2019 Jan 2;8(1):7-14. Epub 2018 Oct 2.

Department of Chemistry and Applied Biosciences Swiss Federal Institute of Technology (ETH) Zurich Vladimir-Prelog-Weg 4 8093 Zurich Switzerland.

The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR-targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter-propagation artificial neural network, a -nearest neighbor learner, and a three-dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top-ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit-to-lead expansion.
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http://dx.doi.org/10.1002/open.201800156DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6317935PMC
January 2019

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning.

ChemistryOpen 2019 Jan 6;8(1). Epub 2018 Dec 6.

Department of Chemistry and Applied Biosciences Swiss Federal Institute of Technology (ETH) Zurich Vladimir-Prelog-Weg 4 8093 Zurich Switzerland.

Invited for this month's cover picture is the group of Prof. Dr. Gisbert Schneider from the Swiss Federal Institute of Technology (ETH) Zurich (Switzerland). The cover picture illustrates the application of machine-learning methods to expand the chemical space of farnesoid X receptor (FXR)-targeting small molecules, by employing an ensemble of three complementary machine-learning approaches (counter-propagation artificial neural network, k-nearest neighbor learner, and three-dimensional pharmacophore model). Read the full text of their Full Paper at 10.1002/open.201800156.
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http://dx.doi.org/10.1002/open.201800270DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6317921PMC
January 2019

In Silico Target Prediction for Small Molecules.

Methods Mol Biol 2019 ;1888:273-309

Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.
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http://dx.doi.org/10.1007/978-1-4939-8891-4_16DOI Listing
June 2019

Simulated Molecular Evolution for Anticancer Peptide Design.

Angew Chem Int Ed Engl 2019 02 9;58(6):1674-1678. Epub 2019 Jan 9.

Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.

A computational technique based on a simulated molecular evolution protocol was employed for anticancer peptide (ACP) design. Starting from known ACPs, innovative bioactive peptides were automatically generated in computer-assisted design-synthesize-test cycles. This design algorithm offers a viable strategy for the generation of novel peptide sequences, without requiring a priori structure-activity knowledge. Sequence morphing and activity improvement were achieved through iterative amino acid variation and selection. Results show that not only the interaction of ACPs with the target membrane is important for their anticancer activity, but also the degree of peptide dimerization, which was corroborated by temperature profiling and electrospray mass spectrometry.
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http://dx.doi.org/10.1002/anie.201811215DOI Listing
February 2019

Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths.

Mol Inform 2019 04 25;38(4):e1800115. Epub 2018 Nov 25.

Bayer AG, Pharmaceuticals, R&D, 42096, Wuppertal, Germany.

We present two approaches for the computation of hydrogen bond acceptor strengths, one by machine-learning and one by a composite quantum-mechanical protocol, both based on the well-established pK scale and dataset. The QM calculations after a necessary linear fit reproduce the complexation free energies in solution with an RMSE of 2.6 kJ mol , not far off the expected error of 2 kJ mol obtained from the comparison of experimental data from two different sources. The second approach is by Gaussian Process Regression (GPR) machine-learning. We describe the hydrogen bond acceptor atoms by a radial atomic reactivity descriptor that encodes their electronic and steric environment. The performance of the GPR model on an external test set corresponds to 3.3 kJ mol , which is also close to the experimental error. We apply the GPR model built on experimental data to model the hydrogen bond acceptor strengths of a series of hydrogen bond acceptor sites of 10 phosphodiesterase 10 A inhibitors. The predicted values correlate well with the experimentally measured IC values.
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http://dx.doi.org/10.1002/minf.201800115DOI Listing
April 2019

Scaffold-Hopping from Synthetic Drugs by Holistic Molecular Representation.

Sci Rep 2018 11 7;8(1):16469. Epub 2018 Nov 7.

Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.

The discovery of novel ligand chemotypes allows to explore uncharted regions in chemical space, thereby potentially improving synthetic accessibility, potency, and the drug-likeness of molecules. Here, we demonstrate the scaffold-hopping ability of the new Weighted Holistic Atom Localization and Entity Shape (WHALES) molecular descriptors compared to seven state-of-the-art molecular representations on 30,000 compounds and 182 biological targets. In a prospective application, we apply WHALES to the discovery of novel retinoid X receptor (RXR) modulators. WHALES descriptors identified four agonists with innovative molecular scaffolds, populating uncharted regions of the chemical space. One of the agonists, possessing a rare non-acidic chemotype, revealed high selectivity on 12 nuclear receptors and comparable efficacy as bexarotene on induction of ATP-binding cassette transporter A1, angiopoietin like protein 4 and apolipoprotein E. The outcome of this research supports WHALES as an innovative tool to explore novel regions of the chemical space and to detect novel bioactive chemotypes by straightforward similarity searching.
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http://dx.doi.org/10.1038/s41598-018-34677-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220272PMC
November 2018

Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning.

Medchemcomm 2018 Sep 22;9(9):1538-1546. Epub 2018 Aug 22.

Swiss Federal Institute of Technology (ETH) , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , 8093 Zürich , Switzerland . Email:

Lipophilicity prediction is routinely applied to small molecules and presents a working alternative to experimental log  or log  determination. For compounds outside the domain of classical medicinal chemistry these predictions lack accuracy, advocating the development of bespoke approaches. Peptides and their derivatives and mimetics fill the structural gap between small synthetic drugs and genetically engineered macromolecules. Here, we present a data-driven machine learning method for peptide log  prediction. A model for estimating the lipophilicity of short linear peptides consisting of natural amino acids was developed. In a prospective test, we obtained accurate predictions for a set of newly synthesized linear tri- to hexapeptides. Further model development focused on more complex peptide mimetics from the AstraZeneca compound collection. The results obtained demonstrate the applicability of the new prediction model to peptides and peptide derivatives in a log  range of approximately -3 to 5, with superior accuracy to established lipophilicity models for small molecules.
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http://dx.doi.org/10.1039/c8md00370jDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6151477PMC
September 2018

Combined Proteomic and In Silico Target Identification Reveal a Role for 5-Lipoxygenase in Developmental Signaling Pathways.

Cell Chem Biol 2018 09 28;25(9):1095-1106.e23. Epub 2018 Jun 28.

Max Planck Institut für Molekulare Physiologie, Otto-Hahn-Strasse 11, Dortmund 44227, Germany; Technische Universität Dortmund, Fakultät für Chemie und Chemische Biologie, Otto-Hahn-Strasse 6, Dortmund 44227, Germany. Electronic address:

Identification and validation of the targets of bioactive small molecules identified in cell-based screening is challenging and often meets with failure, calling for the development of new methodology. We demonstrate that a combination of chemical proteomics with in silico target prediction employing the SPiDER method may provide efficient guidance for target candidate selection and prioritization for experimental in-depth evaluation. We identify 5-lipoxygenase (5-LO) as the target of the Wnt pathway inhibitor Lipoxygenin. Lipoxygenin is a non-redox 5-LO inhibitor, modulates the β-catenin-5-LO complex and induces reduction of both β-catenin and 5-LO levels in the nucleus. Lipoxygenin and the structurally unrelated 5-LO inhibitor CJ-13,610 promote cardiac differentiation of human induced pluripotent stem cells and inhibit Hedgehog, TGF-β, BMP, and Activin A signaling, suggesting an unexpected and yet unknown role of 5-LO in these developmental pathways.
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http://dx.doi.org/10.1016/j.chembiol.2018.05.016DOI Listing
September 2018

MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes.

ChemMedChem 2018 11 2;13(21):2281-2289. Epub 2018 Oct 2.

Bayer AG, Pharmaceuticals, Research & Development, 42096, Wuppertal, Germany.

The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that can predict both phase I and phase II reaction sites of drugs in a single prediction run. We developed cheminformatics workflows to filter and process reactions to obtain suitable phase I and phase II data sets for model training. Employing a recently developed molecular representation based on quantum chemical partial charges, we constructed random forest machine learning models for phase I and phase II reactions. After combining these models with our previous cytochrome P450 model and calibrating the combination against Bayer in-house data, we obtained the MetScore model that shows good performance, with Matthews correlation coefficients of 0.61 and 0.76 for diverse phase I and phase II reaction types, respectively. We validated its potential applicability to lead optimization campaigns for a new and independent data set compiled from recent publications. The results of this study demonstrate the usefulness of quantum-chemistry-derived molecular representations for reactivity prediction.
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http://dx.doi.org/10.1002/cmdc.201800309DOI Listing
November 2018