MedChemica Ltd., Alderley Park, Macclesfield, Cheshire SK10 4TG, U.K.
J Med Chem. 2020 Aug 27;63(16):8695-8704. doi: 10.1021/acs.jmedchem.0c00163. Epub 2020 Jun 11.
The latest developments in artificial intelligence (AI) have arrived into an existing state of creative tension between computational and medicinal chemists. At their most productive, medicinal and computational chemists have made significant progress in delivering new therapeutic agents into the clinic. However, the relationship between these communities has the prospect of being weakened by application of oversimplistic AI methods that, if they fail to deliver, will reinforce unproductive prejudices. We review what can be learned from our history of integrating QSAR and structure-based methods into drug discovery. Now with synthesis and testing available as contract services, the environment for computational innovation has changed and we consider the impact this may have on the relationships in our disciplines. We discuss the current state of interdisciplinary communication and suggest approaches to bring the subdisciplines together in order to improve computational medicinal chemistry and, most importantly, deliver better medicines to the clinic faster.
人工智能（AI）的最新进展已经进入到计算化学家和药物化学家之间现有的创造性紧张关系之中。在最具成效的情况下，药物和计算化学家在将新的治疗药物推向临床方面取得了重大进展。然而，这些社区之间的关系有可能因应用过于简单化的 AI 方法而削弱，如果这些方法未能成功，将强化无益的偏见。我们回顾了从将 QSAR 和基于结构的方法整合到药物发现中可以学到的经验。现在，由于合成和测试可以作为合同服务获得，计算创新的环境已经发生了变化，我们考虑这可能对我们学科中的关系产生的影响。我们讨论了目前跨学科交流的状况，并提出了一些方法，将这些子学科结合起来，以提高计算药物化学的水平，最重要的是，更快地将更好的药物推向临床。
