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.

尽管在制药行业中已经广泛使用计算方法，但迫切需要能够改善和优化药物发现和开发流程的新方法。尽管没有独特的解决方案来满足这种创新需求，但近来人们对将人工智能用于此目的表现出了浓厚的兴趣。事实上，不仅科学界在这方面做出了重大贡献，而且制药业与人工智能公司之间的伙伴关系也在不断发展。除了这些贡献和努力之外，还有一个潜在的问题，我们打算在这次审查中讨论：人工智能的实施能否克服药物发现过程中的内在困难？尽管这是一个悬而未决的问题，但在这项工作中，我们将重点关注这些算法在早期药物发现方面相对于传统方法所提供的优势。