Abstract

The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there is an urgent need to improve this process using innovative technologies such as artificial intelligence (AI). Different AI tools are being applied to support all four steps of the drug development process (basic research for drug discovery; pre-clinical phase; clinical phase; and postmarketing). Some of the main tasks where AI has proven useful include identifying molecular targets, searching for hit and lead compounds, synthesising drug-like compounds and predicting ADME-Tox. This review, on the one hand, brings in a mathematical vision of some of the key AI methods used in drug development closer to medicinal chemists and, on the other hand, brings the drug development process and the use of different models closer to mathematicians. Emphasis is placed on two aspects not mentioned in similar surveys, namely, Bayesian approaches and their applications to molecular modelling and the eventual final use of the methods to actually support decisions.

Graphic abstract

Promoting a perfect synergy

中文翻译：

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药物开发中的人工智能：多学科视角

摘要

将新药推向商业市场需要经历一个复杂而漫长的过程，通常会持续数年，并且由于高损耗率而需要巨额资金成本。因此，迫切需要利用人工智能（AI）等创新技术来改进这一流程。不同的人工智能工具被应用于支持药物开发过程的所有四个步骤（药物发现的基础研究；临床前阶段；临床阶段；和上市后）。人工智能已被证明有用的一些主要任务包括识别分子靶标、搜索命中化合物和先导化合物、合成类药物化合物和预测 ADME-Tox。这篇综述一方面使药物开发中使用的一些关键人工智能方法的数学视野更接近药物化学家，另一方面使药物开发过程和不同模型的使用更接近数学家。重点放在类似调查中未提及的两个方面，即贝叶斯方法及其在分子建模中的应用以及这些方法最终用于实际支持决策的最终用途。

图文摘要

促进完美协同