Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina; Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina; Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina.
Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina; Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina.
Arch Biochem Biophys. 2021 Feb 15;698:108730. doi: 10.1016/j.abb.2020.108730. Epub 2020 Dec 19.
Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.
尽管计算方法在制药行业中的应用已经得到了很好的建立,但仍迫切需要新的方法来改进和优化药物发现和开发的管道。尽管没有针对这种创新需求的独特解决方案,但最近人们对人工智能在这方面的应用产生了浓厚的兴趣。事实上,不仅科学界在这方面做出了重大贡献,制药行业和人工智能公司之间的合作关系也在不断发展。除了这些贡献和努力之外,还有一个潜在的问题,我们打算在这篇综述中讨论:人工智能的实施能否克服药物发现过程中的内在困难?虽然这是一个悬而未决的问题,但在这项工作中,我们将重点讨论这些算法在早期药物发现方面相对于传统方法的优势。