Machine learning (ML) is a promising approach for predicting small molecule
properties in drug discovery. Here, we provide a comprehensive overview of
various ML methods introduced for this purpose in recent years. We review a
wide range of properties, including binding affinities, solubility, and ADMET
(Absorption, Distribution, Metabolism, Excretion, and Toxicity). We discuss
existing popular datasets and molecular descriptors and embeddings, such as
chemical fingerprints and graph-based neural networks. We highlight also
challenges of predicting and optimizing multiple properties during hit-to-lead
and lead optimization stages of drug discovery and explore briefly possible
multi-objective optimization techniques that can be used to balance diverse
properties while optimizing lead candidates. Finally, techniques to provide an
understanding of model predictions, especially for critical decision-making in
drug discovery are assessed. Overall, this review provides insights into the
landscape of ML models for small molecule property predictions in drug
discovery. So far, there are multiple diverse approaches, but their
performances are often comparable. Neural networks, while more flexible, do not
always outperform simpler models. This shows that the availability of
high-quality training data remains crucial for training accurate models and
there is a need for standardized benchmarks, additional performance metrics,
and best practices to enable richer comparisons between the different
techniques and models that can shed a better light on the differences between
the many techniques.

机器学习在药物发现中预测小分子性质方面具有很大潜力。本文提供了对近年来为此目的引入的各种机器学习方法的综合概述，并对结合亲合力、溶解度和 ADMET（吸收、分布、代谢、排泄和毒性）等多个性质预测和优化技术进行了讨论。我们还评估了模型预测方面的技术，特别是在药物发现中关键决策方面提供模型预测理解的技术。总的来说，这篇综述为药物发现中小分子性质预测的机器学习模型提供了深入了解。尽管有多种多样的方法，但它们的性能通常相当。神经网络虽然更加灵活，但并不总是优于更简单的模型。这表明高质量的训练数据对于训练准确的模型仍然至关重要，并且需要标准化的基准、额外的性能指标和最佳实践，以便更好地比较不同技术和模型之间的差异。