DrugDiff: small molecule diffusion model with flexible guidance towards molecular properties

IF 5.7 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Marie Oestreich, Erinc Merdivan, Michael Lee, Joachim L. Schultze, Marie Piraud, Matthias Becker
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引用次数: 0

Abstract

With the cost/yield-ratio of drug development becoming increasingly unfavourable, recent work has explored machine learning to accelerate early stages of the development process. Given the current success of deep generative models across domains, we here investigated their application to the property-based proposal of new small molecules for drug development. Specifically, we trained a latent diffusion model—DrugDiff—paired with predictor guidance to generate novel compounds with a variety of desired molecular properties. The architecture was designed to be highly flexible and easily adaptable to future scenarios. Our experiments showed successful generation of unique, diverse and novel small molecules with targeted properties. The code is available at https://github.com/MarieOestreich/DrugDiff.

This work expands the use of generative modelling in the field of drug development from previously introduced models for proteins and RNA to the here presented application to small molecules. With small molecules making up the majority of drugs, but simultaneously being difficult to model due to their elaborate chemical rules, this work tackles a new level of difficulty in comparison to sequence-based molecule generation as is the case for proteins and RNA. Additionally, the demonstrated framework is highly flexible, allowing easy addition or removal of considered molecular properties without the need to retrain the model, making it highly adaptable to diverse research settings and it shows compelling performance for a wide variety of targeted molecular properties.

DrugDiff:对分子性质具有灵活指导的小分子扩散模型
随着药物开发的成本/收益比变得越来越不利,最近的工作已经探索了机器学习来加速开发过程的早期阶段。鉴于目前跨领域的深度生成模型的成功,我们在这里研究了它们在基于属性的药物开发新小分子建议中的应用。具体来说,我们训练了一个潜在扩散模型-药物差异-与预测器指导配对,以产生具有各种所需分子特性的新化合物。该体系结构的设计高度灵活,易于适应未来的场景。我们的实验成功地生成了独特的、多样的、新颖的具有目标特性的小分子。代码可在https://github.com/MarieOestreich/DrugDiff上获得。这项工作扩大了生成模型在药物开发领域的使用,从以前介绍的蛋白质和RNA模型到这里介绍的小分子应用。由于小分子构成了大多数药物,但同时由于其复杂的化学规则而难以建模,与基于序列的分子生成(如蛋白质和RNA)相比,这项工作解决了一个新的难度水平。此外,所演示的框架非常灵活,允许轻松添加或删除所考虑的分子特性,而无需重新训练模型,使其高度适应各种研究设置,并显示出各种目标分子特性的引人注目的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
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