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Ginex T, Vázquez J, Estarellas C, Luque FJ. Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design. Curr Opin Struct Biol 2024; 87:102870. [PMID: 38914031 DOI: 10.1016/j.sbi.2024.102870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 06/02/2024] [Accepted: 06/04/2024] [Indexed: 06/26/2024]
Abstract
The expansion of the chemical space to tangible libraries containing billions of synthesizable molecules opens exciting opportunities for drug discovery, but also challenges the power of computer-aided drug design to prioritize the best candidates. This directly hits quantum mechanics (QM) methods, which provide chemically accurate properties, but subject to small-sized systems. Preserving accuracy while optimizing the computational cost is at the heart of many efforts to develop high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM-tailored physics-based force fields and the coupling of QM with machine learning, in conjunction with the computing performance of supercomputing resources, will enhance the ability to use these methods in drug discovery. The challenge is formidable, but we will undoubtedly see impressive advances that will define a new era.
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Affiliation(s)
- Tiziana Ginex
- Pharmacelera, Parc Científic de Barcelona (PCB), Baldiri Reixac 4-8, 08028 Barcelona, Spain
| | - Javier Vázquez
- Pharmacelera, Parc Científic de Barcelona (PCB), Baldiri Reixac 4-8, 08028 Barcelona, Spain; Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain
| | - Carolina Estarellas
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Química Teòrica i Computacional (IQTCUB), 08921 Santa Coloma de Gramenet, Spain
| | - F Javier Luque
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Química Teòrica i Computacional (IQTCUB), 08921 Santa Coloma de Gramenet, Spain.
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Bassani D, Parrott NJ, Manevski N, Zhang JD. Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules. Expert Opin Drug Discov 2024; 19:683-698. [PMID: 38727016 DOI: 10.1080/17460441.2024.2348157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/23/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Neil John Parrott
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Nenad Manevski
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Jitao David Zhang
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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Heid E, Greenman KP, Chung Y, Li SC, Graff DE, Vermeire FH, Wu H, Green WH, McGill CJ. Chemprop: A Machine Learning Package for Chemical Property Prediction. J Chem Inf Model 2024; 64:9-17. [PMID: 38147829 PMCID: PMC10777403 DOI: 10.1021/acs.jcim.3c01250] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/28/2023]
Abstract
Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.
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Affiliation(s)
- Esther Heid
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
- Institute
of Materials Chemistry, TU Wien, 1060 Vienna, Austria
| | - Kevin P. Greenman
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Yunsie Chung
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Shih-Cheng Li
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Chemical Engineering, National Taiwan
University, Taipei 10617, Taiwan
| | - David E. Graff
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Chemistry and Chemical Biology, Harvard
University, Cambridge, Massachusetts 02138, United States
| | - Florence H. Vermeire
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, B-3001 Leuven, Belgium
| | - Haoyang Wu
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - William H. Green
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Charles J. McGill
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, Virginia 23284, United States
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Fliszkiewicz B, Sajdak M. Fragments quantum descriptors in classification of bio-accumulative compounds. J Mol Graph Model 2023; 125:108584. [PMID: 37611341 DOI: 10.1016/j.jmgm.2023.108584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/24/2023] [Accepted: 07/29/2023] [Indexed: 08/25/2023]
Abstract
The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical calculations conducted with Psi4 quantum chemistry package was also added to the quantum properties database. Classification results are compared with a baseline of random guesses and predictions obtained with the traditional RDKit generated molecular descriptors. Chosen classification metrics show that results obtained with fragments quantum descriptors fall between results from baseline and those provided by molecular descriptors widely applied in cheminformatics. According to the results, the implementation of principal component analysis, causes a drop in categorization metrics.
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Affiliation(s)
- Bartłomiej Fliszkiewicz
- Department of New Technologies and Chemistry, Military University of Technology, Kaliskiego 2, Warsaw, 00-908, Poland.
| | - Marcin Sajdak
- Faculty of Energy and Environmental Engineering, Silesian University of Technology, Akademicka 2A, Gliwice, 44-109, Poland; School of Chemical Engineering, University of Birmingham, S W Campus, Birmingham, B15 TT, United Kingdom
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Park H, Hong S, Lee M, Kang S, Brahma R, Cho KH, Shin JM. AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors. Sci Rep 2023; 13:10268. [PMID: 37355672 PMCID: PMC10290719 DOI: 10.1038/s41598-023-37456-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/22/2023] [Indexed: 06/26/2023] Open
Abstract
The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.
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Affiliation(s)
- Hyejin Park
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Sujeong Hong
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Myeonghun Lee
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Sungil Kang
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Rahul Brahma
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Kwang-Hwi Cho
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Jae-Min Shin
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea.
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Obrezanova O. Artificial intelligence for compound pharmacokinetics prediction. Curr Opin Struct Biol 2023; 79:102546. [PMID: 36804676 DOI: 10.1016/j.sbi.2023.102546] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/04/2023] [Accepted: 01/13/2023] [Indexed: 02/17/2023]
Abstract
Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learning and artificial intelligence (AI) emerged as a major tool for modelling of in vivo animal and human PK, enabling prediction from chemical structure early in drug discovery, and therefore offering opportunities to guide the design and prioritisation of molecules based on relevant in vivo properties and, ultimately, predicting human PK at the point of design. This review presents recent advances in machine learning and AI models for in vivo animal and human PK for small-molecule compounds as well as some examples for antibody therapeutics.
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Affiliation(s)
- Olga Obrezanova
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, CB4 0WJ, UK.
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Cournia Z, Soares TA, Wahab HA, Amaro RE. Celebrating Diversity, Equity, Inclusion, and Respect in Computational and Theoretical Chemistry. J Chem Inf Model 2022; 62:6287-6291. [PMID: 36567670 DOI: 10.1021/acs.jcim.2c01543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Thereza A Soares
- Department of Chemistry, University of São Paulo, 14040-901 Ribeirão Preto, Brazil.,Hylleraas Centre for Quantum Molecular Sciences, University of Oslo, 0315 Oslo, Norway
| | - Habibah A Wahab
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800 Minden, Pulau Pinang, Malaysia
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California, 3234 Urey Hall, #0340, 9500 Gilman Drive, La Jolla, 92093-0340 San Diego, California, United States
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Soares TA, Nunes-Alves A, Mazzolari A, Ruggiu F, Wei GW, Merz K. The (Re)-Evolution of Quantitative Structure-Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods. J Chem Inf Model 2022; 62:5317-5320. [PMID: 36437763 DOI: 10.1021/acs.jcim.2c01422] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Thereza A Soares
- Department of Chemistry, University of São Paulo, Ribeirão Preto 055508-090, Brazil.,Hylleraas Centre for Quantum Molecular Sciences, University of Oslo, Oslo 0315, Norway
| | - Ariane Nunes-Alves
- Institute of Chemistry, Technische Universität Berlin, Berlin 10623, Germany
| | - Angelica Mazzolari
- Department of Pharmaceutical Sciences, University of Milan, Via Mangiagalli 25, Milan I-20133, Italy
| | - Fiorella Ruggiu
- Insitro Inc., 279 East Grand Avenue, South San Francisco 94080, California, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing 48824, Michigan, United States
| | - Kenneth Merz
- Department of Chemistry, Michigan State University, East Lansing 48824, Michigan, United States
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