Artificial intelligence in chemistry and drug design

The discovery of molecular structures with desired properties for applications in drug discovery, crop protection, or chemical biology is among the most impactful scientific challenges. However, given the complexity of biological systems and the associated cost for experiments and trials, molecular design is also scientifically very challenging, prone to failure, inherently expensive and time consuming [1, 2]. To improve our odds and the timelines in this process, and to identify good starting points, unbiased incorporation of knowledge through continuous analysis of literature and patents from different scientific fields is required [3]. The number of yearly publications is increasing, and a good collaboration between scientific experts across disciplines is required to fully evaluate the potential of a hypothesis. The theoretical space of chemistry, even when limited by molecular size, is huge [4] and dramatically exceeds what we can assess experimentally and even computationally. How to navigate through it efficiently and select molecules that satisfy the multiple parameters that need to be optimized and that are synthetically accessible [5]? The number of existing data points at the beginning of a project are low. How can we enrich projects in short time frames with informative molecules and data that are subsequently used to drive the design? With these questions in mind, it comes as no surprise that data mining and statistics have been integrated into molecular discovery and design pipelines to provide computational support in the prioritization of molecular hypotheses [6, 7]. Machine learning algorithms have been part of the routine toolbox of computational and medicinal chemists for decades. The recent increase in applications and coverage of these methodologies has been attributed to advances in computational power, the growing amount of digitized research data, and an increasing theoretical understanding of the algorithms and their shortcomings. However, given the gradual character of these evolutions, it might be counterintuitive to expect a dramatic revolution of molecular design. Nevertheless, extravagant claims have been made for the ability of Artificial Intelligence (AI) to accelerate the design process [8, 9]; how well founded are these claims? While there is unquestionably a lot of potential in novel computational tools, it is important to scrutinize them and compare their performance to already existing methods, to objectively distinguish real progress from promotion. Only such careful evaluations will enable us to shed light on whether novel artificial intelligence methods contribute to an evolution or a revolution of the established scientific discipline of computer-assisted molecular design [10].