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Kerstjens A, De Winter H. A molecule perturbation software library and its application to study the effects of molecular design constraints. J Cheminform 2023; 15:89. [PMID: 37752561 PMCID: PMC10523775 DOI: 10.1186/s13321-023-00761-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023] Open
Abstract
Computational molecular design can yield chemically unreasonable compounds when performed carelessly. A popular strategy to mitigate this risk is mimicking reference chemistry. This is commonly achieved by restricting the way in which molecules are constructed or modified. While it is well established that such an approach helps in designing chemically appealing molecules, concerns about these restrictions impacting chemical space exploration negatively linger. In this work we present a software library for constrained graph-based molecule manipulation and showcase its functionality by developing a molecule generator. Said generator designs molecules mimicking reference chemical features of differing granularity. We find that restricting molecular construction lightly, beyond the usual positive effects on drug-likeness and synthesizability of designed molecules, provides guidance to optimization algorithms navigating chemical space. Nonetheless, restricting molecular construction excessively can indeed hinder effective chemical space exploration.
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Affiliation(s)
- Alan Kerstjens
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitslaan 1, 2610, Wilrijk, Belgium
| | - Hans De Winter
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitslaan 1, 2610, Wilrijk, Belgium.
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Fujita T, Terayama K, Sumita M, Tamura R, Nakamura Y, Naito M, Tsuda K. Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring. Sci Technol Adv Mater 2022; 23:352-360. [PMID: 35693890 PMCID: PMC9176351 DOI: 10.1080/14686996.2022.2075240] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 06/15/2023]
Abstract
Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM). Given a time series of generated molecules, CFGM monitors statistically enriched functional groups in comparison to the training data. In the task of absorption wavelength maximization of pure organic molecules (consisting of H, C, N, and O), we successfully identified a strategic change from diketone and aniline derivatives to quinone derivatives. In addition, CFGM led us to a hypothesis that 1,2-quinone is an unconventional chromophore, which was verified with chemical synthesis. This study shows the possibility that human experts can learn from DNMGs to expand their ability to discover functional molecules.
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Affiliation(s)
- Takehiro Fujita
- Polymer Design Group, Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS)Data-driven, Tsukuba, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Japan
- RIKEN Center for Advanced Intelligence Project, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Masato Sumita
- RIKEN Center for Advanced Intelligence Project, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan
| | - Ryo Tamura
- RIKEN Center for Advanced Intelligence Project, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS), Tsukuba, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Yasuyuki Nakamura
- Polymer Design Group, Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS)Data-driven, Tsukuba, Japan
| | - Masanobu Naito
- Polymer Design Group, Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS)Data-driven, Tsukuba, Japan
| | - Koji Tsuda
- RIKEN Center for Advanced Intelligence Project, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS), Tsukuba, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
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Thomas M, Boardman A, Garcia-Ortegon M, Yang H, de Graaf C, Bender A. Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges. Methods Mol Biol 2021; 2390:1-59. [PMID: 34731463 DOI: 10.1007/978-1-0716-1787-8_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Andrew Boardman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Miguel Garcia-Ortegon
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.,Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Hongbin Yang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | | | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
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Renz P, Van Rompaey D, Wegner JK, Hochreiter S, Klambauer G. On failure modes in molecule generation and optimization. Drug Discov Today Technol 2020; 32-33:55-63. [PMID: 33386095 DOI: 10.1016/j.ddtec.2020.09.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/08/2020] [Accepted: 09/14/2020] [Indexed: 11/29/2022]
Abstract
There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation of generative models remains challenging and suggested performance metrics or scoring functions often do not cover all relevant aspects of drug design projects. In this work, we highlight some unintended failure modes in molecular generation and optimization and how these evade detection by current performance metrics.
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Affiliation(s)
- Philipp Renz
- LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenberger Strasse 69, A-4040 Linz, Austria
| | - Dries Van Rompaey
- High Dimensional Biology and Discovery Data Sciences, Janssen Research & Development, Janssen Pharmaceutica N.V., Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Jörg Kurt Wegner
- High Dimensional Biology and Discovery Data Sciences, Janssen Research & Development, Janssen Pharmaceutica N.V., Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Sepp Hochreiter
- LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenberger Strasse 69, A-4040 Linz, Austria
| | - Günter Klambauer
- LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenberger Strasse 69, A-4040 Linz, Austria
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