Chemical Decision Making: An exploration into the modeling and quantification of modern drug discovery

Abstract

Drug discovery is the process of producing compounds with desired biological properties towards diseases of interest. Considering the enormous number of potential structures one could produce, the achievements seen in modern pharmaceutical science are astounding. Despite this, the immense amount of resources required to develop a drug compound presents a bottleneck in the development of pharmaceutical compounds. Computational methods, such as machine learning, offer a potential solution to this problem. Their major advantage is that, by proposing structures in silico, they can narrow the search space, reducing the number of compounds required to be synthesized and tested. However, the use of such methods is often encumbered with issues of synthetic feasibility. To aid in this issue, we present the algorithm DINGOS (Design of Innovative NCEs Generated through Optimization Strategies). DINGOS combines the predictive capabilities of computational methods with a rule-based model of synthesizability. DINGOS is a ligand-based scoring method, generating structures which are similar to the template ligand, while within the provided definition of synthesizability. The functionality of the DINGOS algorithm was demonstrated in a case-study, in which designs were proposed for four compounds (alectinib, cariprazine, osimertinib, and pimavanserin). For each template, a set of 300 designs was proposed, out of which, above 50% were predicted as active by target prediction. Three out of four selected candidate structures were successfully synthesized with the synthetic pathway predicted by DINGOS. Of three synthesized compounds, one showed activity towards the 5-HT2B serotonin receptor. The modular nature of the DINGOS algorithm was demonstrated in a series of follow-up studies. In the first follow-up study, DINGOS was paired with a custom automated synthesis system in order to generate de novo designs within an autonomous active learning drug design cycle. Two rounds of automated synthesis were performed, generating a total of 22 novel compounds, of which five showed micromolar activity towards carbonic anhydrase II. The method was further extended, first by modifying the core predictive component of the algorithm, allowing for the internal representation of the scoring function to be flexibly changed, and in the second, DINGOS was combined with a MCTS (Monte Carlo tree search) algorithm in order to allow for non-greedy optimization of the designed structures. The results of this work showcase the potential of the DINGOS algorithm as a method for generating synthetically feasible de novo designs, and highlights DINGOS’ ability to be adapted to a variety of different drug design problems, narrowing the gap between in silico and experimental drug design.