School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.
Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
Methods Mol Biol. 2024;2714:269-294. doi: 10.1007/978-1-0716-3441-7_15.
The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.
药物发现中最大的挑战仍然是该过程不同阶段的高淘汰率，这每年使行业损失数十亿美元。虽然所有阶段对于确保最终产品的制药级安全性、质量和疗效都至关重要，但将这些努力简化为具有成功潜力的化合物对于更高效和更具成本效益的过程至关重要。制药行业中人工智能（AI）的使用正是针对这一点，并且在临床前筛选生物活性、优化药代动力学特性以改善药物配方、早期毒性预测以减少淘汰率以及预先筛选生物靶标中的遗传变化以提高治疗效果方面都有应用。在这里，我们提出了一系列用于解决小分子开发中这些应用的计算工具，并描述了如何将它们嵌入到当前的药物开发管道中。
