1
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Suzuki T, Ma D, Yasuo N, Sekijima M. Mothra: Multiobjective de novo Molecular Generation Using Monte Carlo Tree Search. J Chem Inf Model 2024; 64:7291-7302. [PMID: 39317969 PMCID: PMC11481094 DOI: 10.1021/acs.jcim.4c00759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
In the field of drug discovery, identifying compounds that satisfy multiple criteria, such as target protein affinity, pharmacokinetics, and membrane permeability, is challenging because of the vast chemical space. Until now, multiobjective optimization via generative models has often involved linear combinations of different reward functions. Linear combinations solve multiobjective optimization problems by turning multiobjective optimization into a single-objective task and causing problems with weighting for each objective. Herein, we propose a scalable multiobjective molecular generative model developed using deep learning techniques. This model integrates the capabilities of recurrent neural networks for molecular generation and Pareto multiobjective Monte Carlo tree search to determine the optimal search direction. Through this integration, our model can generate compounds using enhanced evaluation functions that include important aspects like target protein affinity, drug similarity, and toxicity. The proposed model addresses the limitations of previous linear combination methods, and its effectiveness is demonstrated via extensive experimentation. The improvements achieved in the evaluation metrics underscore the potential utility of our approach toward drug discovery applications. In addition, we provide the source code for our model such that researchers can easily access and use our framework in their own investigations. The source code and pretrained model for Mothra, developed in this study, along with the Docker image for the Pareto front explorer and compound picker, designed to streamline the selection and visualization of optimal chemical compounds, are released under the GNU General Public License v3.0 and available at https://github.com/sekijima-lab/Mothra.
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Affiliation(s)
- Takamasa Suzuki
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8501Japan
| | - Dian Ma
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8501Japan
| | - Nobuaki Yasuo
- Tokyo Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, Tokyo 152-8550, Japan
| | - Masakazu Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8501Japan
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2
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Abstract
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
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Affiliation(s)
- Catrin Hasselgren
- Safety Assessment, Genentech, Inc., South San Francisco, California, USA
| | - Tudor I Oprea
- Expert Systems Inc., San Diego, California, USA;
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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3
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Li B, Ran T, Chen H. 3D based generative PROTAC linker design with reinforcement learning. Brief Bioinform 2023; 24:bbad323. [PMID: 37670499 DOI: 10.1093/bib/bbad323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/06/2023] [Accepted: 08/20/2023] [Indexed: 09/07/2023] Open
Abstract
Proteolysis targeting chimera (PROTAC), has emerged as an effective modality to selectively degrade disease-related proteins by harnessing the ubiquitin-proteasome system. Due to PROTACs' hetero-bifunctional characteristics, in which a linker joins a warhead binding to a protein of interest (POI), conferring specificity and a E3-ligand binding to an E3 ubiquitin ligase, this could trigger the ubiquitination and transportation of POI to the proteasome, followed by degradation. The rational PROTAC linker design is challenging due to its relatively large molecular weight and the complexity of maintaining the binding mode of warhead and E3-ligand in the binding pockets of counterpart. Conventional linker generation method can only generate linkers in either 1D SMILES or 2D graph, without taking into account the information of ternary structures. Here we propose a novel 3D linker generative model PROTAC-INVENT which can not only generate SMILES of PROTAC but also its 3D putative binding conformation coupled with the target protein and the E3 ligase. The model is trained jointly with the RL approach to bias the generation of PROTAC structures toward pre-defined 2D and 3D based properties. Examples were provided to demonstrate the utility of the model for generating reasonable 3D conformation of PROTACs. On the other hand, our results show that the associated workflow for 3D PROTAC conformation generation can also be used as an efficient docking protocol for PROTACs.
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Affiliation(s)
- Baiqing Li
- Guangzhou Laboratory, Guangzhou 510005, Guangdong Province, China
| | - Ting Ran
- Guangzhou Laboratory, Guangzhou 510005, Guangdong Province, China
| | - Hongming Chen
- Guangzhou Laboratory, Guangzhou 510005, Guangdong Province, China
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4
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Hirlekar BU, Nuthi A, Singh KD, Murty US, Dixit VA. An overview of compound properties, multiparameter optimization, and computational drug design methods for PARP-1 inhibitor drugs. Eur J Med Chem 2023; 252:115300. [PMID: 36989813 DOI: 10.1016/j.ejmech.2023.115300] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 03/29/2023]
Abstract
Breast cancer treatment with PARP-1 inhibitors remains challenging due to emerging toxicities, drug resistance, and unaffordable costs of treatment options. How do we invent strategies to design better anti-cancer drugs? A part of the answer is in optimized compound properties, desirability functions, and modern computational drug design methods that drive selectivity and toxicity and have not been reviewed for PARP-1 inhibitors. Nonetheless, comparisons of these compound properties for PARP-1 inhibitors are not available in the literature. In this review, we analyze the physchem, PKPD space to identify inherent desirability functions characteristic of approved drugs that can be valuable for the design of better candidates. Recent literature utilizing ligand, structure-based drug design strategies and matched molecular pair analysis (MMPA) for the discovery of novel PARP-1 inhibitors are also reviewed. Thus, this perspective provides valuable insights into the medchem and multiparameter optimization of PARP-1 inhibitors that might be useful to other medicinal chemists.
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5
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Yoshizawa T, Ishida S, Sato T, Ohta M, Honma T, Terayama K. Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search. J Chem Inf Model 2022; 62:5351-5360. [DOI: 10.1021/acs.jcim.2c00787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Tatsuya Yoshizawa
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
| | - Shoichi Ishida
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
| | - Tomohiro Sato
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama230-0045, Japan
| | - Masateru Ohta
- HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama230-0045, Japan
| | - Teruki Honma
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama230-0045, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
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6
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Beck H, Härter M, Haß B, Schmeck C, Baerfacker L. Small molecules and their impact in drug discovery: A perspective on the occasion of the 125th anniversary of the Bayer Chemical Research Laboratory. Drug Discov Today 2022; 27:1560-1574. [PMID: 35202802 DOI: 10.1016/j.drudis.2022.02.015] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/13/2022] [Accepted: 02/17/2022] [Indexed: 02/07/2023]
Abstract
The year 2021 marks the 125th anniversary of the Bayer Chemical Research Laboratory in Wuppertal, Germany. A significant number of prominent small-molecule drugs, from aspirin to Xarelto, have emerged from this research site. In this review, we shed light on historic cornerstones of small-molecule drug research, discussing current and future trends in drug discovery as well as providing a personal outlook on the future of drug research with a focus on small molecules.
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Affiliation(s)
- Hartmut Beck
- Research & Development, Pharmaceuticals, Bayer AG, Wuppertal, Germany.
| | - Michael Härter
- Research & Development, Pharmaceuticals, Bayer AG, Wuppertal, Germany
| | - Bastian Haß
- Digital & Commercial Innovation, Pharmaceuticals, Bayer AG, Berlin, Germany
| | - Carsten Schmeck
- Research & Development, Pharmaceuticals, Bayer AG, Wuppertal, Germany
| | - Lars Baerfacker
- Research & Development, Pharmaceuticals, Bayer AG, Wuppertal, Germany
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7
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CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding. Nat Rev Chem 2022; 6:287-295. [PMID: 35783295 PMCID: PMC9246350 DOI: 10.1038/s41570-022-00363-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.
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8
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Li WX, Tong X, Yang PP, Zheng Y, Liang JH, Li GH, Liu D, Guan DG, Dai SX. Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods. Aging (Albany NY) 2022; 14:1448-1472. [PMID: 35150482 PMCID: PMC8876917 DOI: 10.18632/aging.203887] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/28/2022] [Indexed: 11/25/2022]
Abstract
Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.
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Affiliation(s)
- Wen-Xing Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, Guangdong, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Xin Tong
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Peng-Peng Yang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Yang Zheng
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Ji-Hao Liang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Gong-Hua Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China
| | - Dahai Liu
- School of Medicine, Foshan University, Foshan 528000, Guangdong, China
| | - Dao-Gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, Guangdong, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Shao-Xing Dai
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
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9
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Hanaoka K. Bayesian optimization for goal-oriented multi-objective inverse material design. iScience 2021; 24:102781. [PMID: 34286234 PMCID: PMC8273421 DOI: 10.1016/j.isci.2021.102781] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 06/01/2021] [Accepted: 06/21/2021] [Indexed: 11/28/2022] Open
Abstract
Bayesian optimization (BO) can accelerate material design requiring time-consuming experiments. However, although most material designs require tuning of multiple properties, the efficiency of multi-objective (MO) BO in time-consuming experimental material design remains unclear, due to the complexity of handling multiple objectives. This study introduces MO BO method that efficiently achieves predefined goals and shows that by focusing on achieving the goals, BO can efficiently accelerate realistic MO design problems with small efforts. Benchmarks showed that the proposed BO method dramatically reduced the number of experiments needed to achieve goals relative to a baseline method. Virtual MO inverse design experiments with realistic material design problems were also performed, during which the proposed method could achieve goals within only around ten experiments in average and showed over 1000-fold acceleration relative to the random sampling for the most difficult case. The introduction of goal-oriented BO will precede real-world application of BO.
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Affiliation(s)
- Kyohei Hanaoka
- Advanced Technology Research & Development Center, Showa Denko Materials Co., Ltd., 48 Wadai, Tsukuba City, Ibaraki Prefecture 300-4247, Japan
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10
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Makara GM, Kovács L, Szabó I, Pőcze G. Derivatization Design of Synthetically Accessible Space for Optimization: In Silico Synthesis vs Deep Generative Design. ACS Med Chem Lett 2021; 12:185-194. [PMID: 33603964 DOI: 10.1021/acsmedchemlett.0c00540] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 12/18/2020] [Indexed: 12/25/2022] Open
Abstract
Molecular design is of utmost importance in lead optimization programs ultimately determining the fate of the project and the speed to reach preclinical stage. Newly designed lead analogues or new chemotypes must successfully address the challenges in the multidimensional optimization process throughout several optimization cycles. The speed, quality, and creativity of the designs can have a major impact on the cycle time, the number of required cycles, and the number of compounds needed to be synthesized and evaluated that in combination affect the overall timeline and cost of the lead optimization phase. Recently, a new concept, generative design with deep learning, has become popular for de novo design of project relevant analogue sets. We have developed a de novo design technology called "derivatization design" that applies artificial-intelligence-assisted forward in silico synthesis for the generation of near neighbor lead analogues as well as scaffold variations. The several attractive features of the methodology include synthetic feasibility, reagent availability and cost data associated with each new molecule; thus, detailed synthetic assessment is automatically generated during the design. As a result, these practically important data types can become an early part of the ranking and selection process for cycle time reduction. The power of derivatization design is demonstrated in a simple design study of DDR1 inhibitors and comparison of the produced molecules to a recently published data set obtained with deep generative design.
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Affiliation(s)
| | | | - István Szabó
- ChemPass Ltd., 7 Záhony St, Budapest 1031, Hungary
| | - Gábor Pőcze
- ChemPass Ltd., 7 Záhony St, Budapest 1031, Hungary
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11
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Mervin LH, Johansson S, Semenova E, Giblin KA, Engkvist O. Uncertainty quantification in drug design. Drug Discov Today 2020; 26:474-489. [PMID: 33253918 DOI: 10.1016/j.drudis.2020.11.027] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/13/2020] [Accepted: 11/23/2020] [Indexed: 01/03/2023]
Abstract
Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design-make-test-analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.
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Affiliation(s)
- Lewis H Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Simon Johansson
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden; Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Elizaveta Semenova
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Kathryn A Giblin
- Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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12
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Blaschke T, Arús-Pous J, Chen H, Margreitter C, Tyrchan C, Engkvist O, Papadopoulos K, Patronov A. REINVENT 2.0: An AI Tool for De Novo Drug Design. J Chem Inf Model 2020; 60:5918-5922. [PMID: 33118816 DOI: 10.1021/acs.jcim.0c00915] [Citation(s) in RCA: 159] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In the past few years, we have witnessed a renaissance of the field of molecular de novo drug design. The advancements in deep learning and artificial intelligence (AI) have triggered an avalanche of ideas on how to translate such techniques to a variety of domains including the field of drug design. A range of architectures have been devised to find the optimal way of generating chemical compounds by using either graph- or string (SMILES)-based representations. With this application note, we aim to offer the community a production-ready tool for de novo design, called REINVENT. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. It can facilitate the idea generation process by bringing to the researcher's attention the most promising compounds. REINVENT's code is publicly available at https://github.com/MolecularAI/Reinvent.
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Affiliation(s)
- Thomas Blaschke
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden
| | - Josep Arús-Pous
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden.,Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
| | - Hongming Chen
- Chemistry and Chemical Biology Centre, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Science Park, 510530 Guangzhou, China
| | - Christian Margreitter
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden
| | - Christian Tyrchan
- Medicinal Chemistry, Early RIA, Biopharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden
| | - Kostas Papadopoulos
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden
| | - Atanas Patronov
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden
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13
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Leguy J, Cauchy T, Glavatskikh M, Duval B, Da Mota B. EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation. J Cheminform 2020; 12:55. [PMID: 33431049 PMCID: PMC7494000 DOI: 10.1186/s13321-020-00458-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 08/31/2020] [Indexed: 11/24/2022] Open
Abstract
The objective of this work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Our method must be flexible to adapt to very different problems. Therefore, it has to be able to work with or without the influence of prior data and knowledge. Moreover, regardless of the success, it should be as interpretable as possible to allow for diagnosis and improvement. We propose here a new open source generation method using an evolutionary algorithm to sequentially build molecular graphs. It is independent of starting data and can generate totally unseen compounds. To be able to search a large part of the chemical space, we define an original set of 7 generic mutations close to the atomic level. Our method achieves excellent performances and even records on the QED, penalised logP, SAscore, CLscore as well as the set of goal-directed functions defined in GuacaMol. To demonstrate its flexibility, we tackle a very different objective issued from the organic molecular materials domain. We show that EvoMol can generate sets of optimised molecules having high energy HOMO or low energy LUMO, starting only from methane. We can also set constraints on a synthesizability score and structural features. Finally, the interpretability of EvoMol allows for the visualisation of its exploration process as a chemically relevant tree. ![]()
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Affiliation(s)
- Jules Leguy
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France
| | - Thomas Cauchy
- Laboratoire MOLTECH-Anjou, UMR CNRS 6200, UNIV Angers, SFR MATRIX, 2 Bd Lavoisier, 49045, Angers, France.
| | - Marta Glavatskikh
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France.,Laboratoire MOLTECH-Anjou, UMR CNRS 6200, UNIV Angers, SFR MATRIX, 2 Bd Lavoisier, 49045, Angers, France
| | - Béatrice Duval
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France
| | - Benoit Da Mota
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France.
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14
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Winter R, Retel J, Noé F, Clevert DA, Steffen A. grünifai: interactive multiparameter optimization of molecules in a continuous vector space. Bioinformatics 2020; 36:4093-4094. [PMID: 32369561 DOI: 10.1093/bioinformatics/btaa271] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 03/09/2020] [Accepted: 04/27/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Optimizing small molecules in a drug discovery project is a notoriously difficult task as multiple molecular properties have to be considered and balanced at the same time. In this work, we present our novel interactive in silico compound optimization platform termed grünifai to support the ideation of the next generation of compounds under the constraints of a multiparameter objective. grünifai integrates adjustable in silico models, a continuous representation of the chemical space, a scalable particle swarm optimization algorithm and the possibility to actively steer the compound optimization through providing feedback on generated intermediate structures. AVAILABILITY AND IMPLEMENTATION Source code and documentation are freely available under an MIT license and are openly available on GitHub (https://github.com/jrwnter/gruenifai). The backend, including the optimization method and distribution on multiple GPU nodes is written in Python 3. The frontend is written in ReactJS.
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Affiliation(s)
- Robin Winter
- Department of Digital Technologies, Bayer AG, Berlin 13353, Germany.,Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin 14195, Germany
| | - Joren Retel
- Department of Digital Technologies, Bayer AG, Berlin 13353, Germany
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin 14195, Germany
| | | | - Andreas Steffen
- Department of Digital Technologies, Bayer AG, Berlin 13353, Germany
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15
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Affiliation(s)
- Marco Foscato
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
| | - Vidar R. Jensen
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
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16
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Winter R, Montanari F, Steffen A, Briem H, Noé F, Clevert DA. Efficient multi-objective molecular optimization in a continuous latent space. Chem Sci 2019; 10:8016-8024. [PMID: 31853357 PMCID: PMC6836962 DOI: 10.1039/c9sc01928f] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 07/02/2019] [Indexed: 12/21/2022] Open
Abstract
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable properties. In this work, we propose a novel method that combines in silico prediction of molecular properties such as biological activity or pharmacokinetics with an in silico optimization algorithm, namely Particle Swarm Optimization. Our method takes a starting compound as input and proposes new molecules with more desirable (predicted) properties. It navigates a machine-learned continuous representation of a drug-like chemical space guided by a defined objective function. The objective function combines multiple in silico prediction models, defined desirability ranges and substructure constraints. We demonstrate that our proposed method is able to consistently find more desirable molecules for the studied tasks in relatively short time. We hope that our method can support medicinal chemists in accelerating and improving the lead optimization process.
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Affiliation(s)
- Robin Winter
- Department of Digital Technologies , Bayer AG , Berlin , Germany .
- Department of Mathematics and Computer Science , Freie Universität Berlin , Berlin , Germany
| | | | - Andreas Steffen
- Department of Digital Technologies , Bayer AG , Berlin , Germany .
| | - Hans Briem
- Department of Digital Technologies , Bayer AG , Berlin , Germany .
| | - Frank Noé
- Department of Mathematics and Computer Science , Freie Universität Berlin , Berlin , Germany
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17
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Wu G, Zhao T, Kang D, Zhang J, Song Y, Namasivayam V, Kongsted J, Pannecouque C, De Clercq E, Poongavanam V, Liu X, Zhan P. Overview of Recent Strategic Advances in Medicinal Chemistry. J Med Chem 2019; 62:9375-9414. [PMID: 31050421 DOI: 10.1021/acs.jmedchem.9b00359] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introducing novel strategies, concepts, and technologies that speed up drug discovery and the drug development cycle is of great importance both in the highly competitive pharmaceutical industry as well as in academia. This Perspective aims to present a "big-picture" overview of recent strategic innovations in medicinal chemistry and drug discovery.
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Affiliation(s)
- Gaochan Wu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Tong Zhao
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Jian Zhang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Yuning Song
- Department of Clinical Pharmacy , Qilu Hospital of Shandong University , 250012 Ji'nan , China
| | - Vigneshwaran Namasivayam
- Pharmaceutical Institute, Pharmaceutical Chemistry II , University of Bonn , 53121 Bonn , Germany
| | - Jacob Kongsted
- Department of Physics, Chemistry, and Pharmacy , University of Southern Denmark , DK-5230 Odense M , Denmark
| | - Christophe Pannecouque
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy , K.U. Leuven , Herestraat 49 Postbus 1043 (09.A097) , B-3000 Leuven , Belgium
| | - Erik De Clercq
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy , K.U. Leuven , Herestraat 49 Postbus 1043 (09.A097) , B-3000 Leuven , Belgium
| | - Vasanthanathan Poongavanam
- Department of Physics, Chemistry, and Pharmacy , University of Southern Denmark , DK-5230 Odense M , Denmark
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
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18
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Dixit VA. A simple model to solve a complex drug toxicity problem. Toxicol Res (Camb) 2018; 8:157-171. [PMID: 30997019 DOI: 10.1039/c8tx00261d] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 11/28/2018] [Indexed: 02/06/2023] Open
Abstract
Linear drug toxicity models like therapeutic index (TI), physicochemical rules (rule of five, 3/75), ligand efficiency indices (LEI), ideal pharmacokinetic (PK) and pharmacodynamic (PD) profiles are widely used in drug discovery and development. In spite of this, predicting drug toxicity at various stages remains challenging and the overall productivity (<20%) and ultimate benefit to the patients remain low. A simple drug toxicity model, "Drug Toxicity Index" (DTI), is developed here using 711 oral drugs. DTI redefines drug toxicity as scaled biphasic and exponential functions of PD, PK and physicochemical parameters. PD, PK and physicochemical toxicity contributions were estimated from the on and off target IC50, maximum unbound plasma drug concentration (free C max), and log D values, respectively. These contributions are then scaled by molar dose and oral bioavailability and the logarithm of the sum of scaled contributions is DTI. Drugs with DTI above the WHO ATC drug category specific average values consistently have toxic profiles, while drugs with DTI below this average are relatively safe. DTI performs better than standard rules for lead optimization, LEI and exposure based TIs in identifying safe and toxic drugs. DTI classifies 392 drugs reported in the US-FDA's Liver Toxicity Knowledge Base (LTKB) with an AUC for ROC curves of 0.91-0.64 for different WHO ATC categories. DTI has been used to predict network meta-analysis results on relative toxicity within/across eight different therapeutic areas. It is useful in understanding PD, PK and physicochemical toxicity contributions and identifying potentially toxic drugs and the toxicity of recently approved drugs. Decision trees are proposed for applying the DTI concept in preclinical drug discovery and clinical trial settings. DTI can potentially reduce failure in drug discovery and might be useful in therapeutic drug monitoring and in xenobiotic and environmental toxicity studies.
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Affiliation(s)
- Vaibhav A Dixit
- Department of Pharmacy , Birla Institute of Technology and Sciences Pilani (BITS Pilani) , Vidya Vihar Campus , Street number 41 , Pilani , 333031 , Rajasthan , India . ; ; Tel: +91 1596 255652 ; Tel: +91-7709129400
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19
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Olotu FA, Munsamy G, Soliman MES. Does Size Really Matter? Probing the Efficacy of Structural Reduction in the Optimization of Bioderived Compounds - A Computational "Proof-of-Concept". Comput Struct Biotechnol J 2018; 16:573-586. [PMID: 30546858 PMCID: PMC6280605 DOI: 10.1016/j.csbj.2018.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/14/2018] [Accepted: 11/18/2018] [Indexed: 02/07/2023] Open
Abstract
Over the years, numerous synthetic approaches have been utilized in drug design to improve the pharmacological properties of naturally derived compounds and most importantly, minimize toxic effects associated with their transition to drugs. The reduction of complex bioderived compounds to simpler bioactive fragments has been identified as a viable strategy to develop lead compounds with improved activities and minimal toxicities. Although this ‘reductive’ strategy has been widely exemplified, underlying biological events remain unresolved, hence the unanswered question remains how does the fragmentation of a natural compound improve its bioactivity and reduce toxicities? Herein, using a combinatorial approach, we initialize a computational “proof-of- concept” to expound the differential pharmacological and antagonistic activities of a natural compound, Anguinomycin D, and its synthetic fragment, SB640 towards Exportin Chromosome Region Maintenance 1 (CRM1). Interestingly, our findings revealed that in comparison with the parent compound, SB640 exhibited improved pharmacological attributes, while toxicities and off-target activities were relatively minimal. Moreover, we observed that the reduced size of SB640 allowed ‘deep access’ at the Nuclear Export Signals (NES) binding groove of CRM1, which favored optimal and proximal positioning towards crucial residues while the presence of the long polyketide tail in Anguinomycin D constrained its burial at the hydrophobic groove. Furthermore, with regards to their antagonistic functions, structural inactivation (rigidity) was more pronounced in CRM1 when bound by SB640 as compared to Anguinomycin D. These findings provide essential insights that portray synthetic fragmentation of natural compounds as a feasible approach towards the discovery of potential leads in disease treatment.
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Affiliation(s)
- Fisayo A Olotu
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Geraldene Munsamy
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Mahmoud E S Soliman
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
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20
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Sánchez-Rodríguez A, Pérez-Castillo Y, Schürer SC, Nicolotti O, Mangiatordi GF, Borges F, Cordeiro MNDS, Tejera E, Medina-Franco JL, Cruz-Monteagudo M. From flamingo dance to (desirable) drug discovery: a nature-inspired approach. Drug Discov Today 2017. [PMID: 28624633 DOI: 10.1016/j.drudis.2017.05.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a 'one-target fixation' to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening.
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Affiliation(s)
- Aminael Sánchez-Rodríguez
- Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador
| | | | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università di Bari Aldo Moro, Bari 072006, Italy
| | | | - Fernanda Borges
- CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal
| | - M Natalia D S Cordeiro
- REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal
| | - Eduardo Tejera
- Facultad de Medicina, Universidad de Las Américas, 170513 Quito, Ecuador
| | - José L Medina-Franco
- Universidad Nacional Autónoma de México, Departamento de Farmacia, Facultad de Química, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Maykel Cruz-Monteagudo
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA; CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal; REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal; Department of General Education, West Coast University-Miami Campus, Doral, FL 33178, USA.
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21
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Aliagas I, Berger R, Goldberg K, Nishimura RT, Reilly J, Richardson P, Richter D, Sherer EC, Sparling BA, Bryan MC. Sustainable Practices in Medicinal Chemistry Part 2: Green by Design. J Med Chem 2017; 60:5955-5968. [DOI: 10.1021/acs.jmedchem.6b01837] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Ignacio Aliagas
- Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Raphaëlle Berger
- MRL, Merck & Co., Inc., 2015 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Kristin Goldberg
- Innovative Medicines Unit, AstraZeneca, Building 310, Milton Science Park, Cambridge, CB4 0FZ, U.K
| | - Rachel T. Nishimura
- Janssen Research & Development, LLC, 3210 Merryfield Row, San Diego, California 92121, United States
| | - John Reilly
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Paul Richardson
- Pfizer Global Research and Development, 10777 Science Center Drive (CB2), San Diego, California 92121, United States
| | - Daniel Richter
- Pfizer Global Research and Development, 10777 Science Center Drive (CB2), San Diego, California 92121, United States
| | - Edward C. Sherer
- MRL, Merck & Co., Inc., P.O. Box 2000, Rahway, New Jersey 07065, United States
| | - Brian A. Sparling
- Amgen, Inc., 360 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Marian C. Bryan
- Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
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22
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Automatically updating predictive modeling workflows support decision-making in drug design. Future Med Chem 2016; 8:1779-96. [DOI: 10.4155/fmc-2016-0070] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure–activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.
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