101
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Pogány P, Arad N, Genway S, Pickett SD. De Novo Molecule Design by Translating from Reduced Graphs to SMILES. J Chem Inf Model 2018; 59:1136-1146. [DOI: 10.1021/acs.jcim.8b00626] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Peter Pogány
- Computational and Modeling Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Herts SG1 2NY, United Kingdom
| | - Navot Arad
- GlaxoSmithKline-Tessella Analytics Partnership, Tessella Ltd, Walkern Road, Stevenage, Herts SG1 3QP, United Kingdom
| | - Sam Genway
- GlaxoSmithKline-Tessella Analytics Partnership, Tessella Ltd, Walkern Road, Stevenage, Herts SG1 3QP, United Kingdom
| | - Stephen D. Pickett
- Computational and Modeling Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Herts SG1 2NY, United Kingdom
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102
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Xu Y, Yao H, Lin K. An overview of neural networks for drug discovery and the inputs used. Expert Opin Drug Discov 2018; 13:1091-1102. [DOI: 10.1080/17460441.2018.1547278] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Yinqiu Xu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Hequan Yao
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Kejiang Lin
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
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103
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Greener JG, Moffat L, Jones DT. Design of metalloproteins and novel protein folds using variational autoencoders. Sci Rep 2018; 8:16189. [PMID: 30385875 PMCID: PMC6212568 DOI: 10.1038/s41598-018-34533-1] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/19/2018] [Indexed: 12/26/2022] Open
Abstract
The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies have exploded in popularity in recent years and are increasingly applicable to biology due to the rise in available data. We attempt to link protein design and deep learning by using variational autoencoders to generate protein sequences conditioned on desired properties. Potential copper and calcium binding sites are added to non-metal binding proteins without human intervention and compared to a hidden Markov model. In another use case, a grammar of protein structures is developed and used to produce sequences for a novel protein topology. One candidate structure is found to be stable by molecular dynamics simulation. The ability of our model to confine the vast search space of protein sequences and to scale easily has the potential to assist in a variety of protein design tasks.
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Affiliation(s)
- Joe G Greener
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK
- Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Lewis Moffat
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK
- Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - David T Jones
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
- Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK.
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104
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Hessler G, Baringhaus KH. Artificial Intelligence in Drug Design. Molecules 2018; 23:E2520. [PMID: 30279331 PMCID: PMC6222615 DOI: 10.3390/molecules23102520] [Citation(s) in RCA: 148] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 09/21/2018] [Accepted: 09/22/2018] [Indexed: 11/23/2022] Open
Abstract
Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.
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Affiliation(s)
- Gerhard Hessler
- R&D, Integrated Drug Discovery, Industriepark Hoechst, 65926 Frankfurt am Main, Germany.
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105
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Abstract
Motivation The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein-ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). Results The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction. Availability and implementation https://github.com/hkmztrk/DeepDTA. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hakime Öztürk
- Department of Computer Engineering, Bogazici University, Istanbul, Turkey
| | - Arzucan Özgür
- Department of Computer Engineering, Bogazici University, Istanbul, Turkey
| | - Elif Ozkirimli
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
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106
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Simón-Vidal L, García-Calvo O, Oteo U, Arrasate S, Lete E, Sotomayor N, González-Díaz H. Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies. J Chem Inf Model 2018; 58:1384-1396. [PMID: 29898360 DOI: 10.1021/acs.jcim.8b00286] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all the molecules involved in a query reaction, compared to a reaction of reference. Thus, a dataset of >100 reactions has been collected for different substrates and internal electrophiles, under different reaction conditions, with a wide range of yields (0-98%). The best PTML model found using General Linear Regression (GLR) has R = 0.88 in training and R = 0.83 in external validation series for 10 000 pairs of query and reference reactions. The PTML model has a final R = 0.95 for all reactions using multiple reactions of reference. We also report a comparative study of linear versus nonlinear PTML models based on artificial neural network (ANN) algorithms. PTML-ANN models (LNN, MLP, RBF) with R ≈ 0.1-0.8 do not outperform the first PMTL model. This result confirms the validity of the linearity of the model. Next, we carried out an experimental and theoretical study of nonreported Parham reactions to illustrate the practical use of the PTML model. A 500 000-point simulation and a Hammett analysis of the reactivity space of Parham reactions are also reported.
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Affiliation(s)
- Lorena Simón-Vidal
- Departamento de Química Orgánica II, Facultad de Ciencia y Tecnología , Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU , Apdo. 644 , 48080 Bilbao , Spain
| | - Oihane García-Calvo
- Departamento de Química Orgánica II, Facultad de Ciencia y Tecnología , Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU , Apdo. 644 , 48080 Bilbao , Spain
| | - Uxue Oteo
- Departamento de Química Orgánica II, Facultad de Ciencia y Tecnología , Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU , Apdo. 644 , 48080 Bilbao , Spain
| | - Sonia Arrasate
- Departamento de Química Orgánica II, Facultad de Ciencia y Tecnología , Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU , Apdo. 644 , 48080 Bilbao , Spain
| | - Esther Lete
- Departamento de Química Orgánica II, Facultad de Ciencia y Tecnología , Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU , Apdo. 644 , 48080 Bilbao , Spain
| | - Nuria Sotomayor
- Departamento de Química Orgánica II, Facultad de Ciencia y Tecnología , Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU , Apdo. 644 , 48080 Bilbao , Spain
| | - Humberto González-Díaz
- Departamento de Química Orgánica II, Facultad de Ciencia y Tecnología , Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU , Apdo. 644 , 48080 Bilbao , Spain.,IKERBASQUE, Basque Foundation for Science, 48011 Bilbao , Spain
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107
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Grisoni F, Neuhaus CS, Gabernet G, Müller AT, Hiss JA, Schneider G. Designing Anticancer Peptides by Constructive Machine Learning. ChemMedChem 2018; 13:1300-1302. [DOI: 10.1002/cmdc.201800204] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Indexed: 11/07/2022]
Affiliation(s)
- Francesca Grisoni
- Swiss Federal Institute of Technology (ETH); Department of Chemistry and Applied Biosciences; Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
- University of Milano-Bicocca; Milano Chemometrics & QSAR Research Group; Department of Earth and Environmental Sciences; 20126 Milan Italy
| | - Claudia S. Neuhaus
- Swiss Federal Institute of Technology (ETH); Department of Chemistry and Applied Biosciences; Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
| | - Gisela Gabernet
- Swiss Federal Institute of Technology (ETH); Department of Chemistry and Applied Biosciences; Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
| | - Alex T. Müller
- Swiss Federal Institute of Technology (ETH); Department of Chemistry and Applied Biosciences; Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
| | - Jan A. Hiss
- Swiss Federal Institute of Technology (ETH); Department of Chemistry and Applied Biosciences; Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH); Department of Chemistry and Applied Biosciences; Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
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