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.

中文翻译：

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DrugDiff：小分子扩散模型，可灵活指导分子特性


随着药物开发的成本/产量比变得越来越不利，最近的工作探索了机器学习以加速开发过程的早期阶段。鉴于当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新小分子药物开发提案中的应用。具体来说，我们训练了一个潜在扩散模型 DrugDiff 与预测器指导相结合，以生成具有各种所需分子特性的新化合物。该架构设计为高度灵活，可轻松适应未来的场景。我们的实验表明，成功生成了具有靶向特性的独特、多样和新颖的小分子。该代码可在 https://github.com/MarieOestreich/DrugDiff 获取。这项工作将生成建模在药物开发领域的使用从以前介绍的蛋白质和 RNA 模型扩展到这里介绍的小分子应用。由于小分子构成了药物的大部分，但同时由于其复杂的化学规则而难以建模，与蛋白质和 RNA 等基于序列的分子生成相比，这项工作解决了新的难度。此外，所演示的框架具有高度的灵活性，无需重新训练模型即可轻松添加或删除所考虑的分子特性，使其高度适应各种研究环境，并且对各种目标分子特性表现出令人信服的性能。