Assessing the impact of generative AI on medicinal chemistry

To the Editor — The profound challenges of drug discovery, coupled with the societal importance of the task, make it imperative that we investigate novel, creative methods that improve our abilities to design new medicines. In recent years, attempts at developing and deploying a wide range of computational methods to support drug discovery have accelerated—sometimes with extraordinary claims made about their significance. Novel computational approaches require rigorous evaluation to determine their true utility in realworld drug discovery settings. Sadly, novel methods often are disclosed without sufficient documentation, making it difficult or impossible to carry out such an objective evaluation. Over the past few years, interest has grown in the application of artificial intelligence (AI) techniques to drug discovery1,2. One active branch of AI that has been the focus of a tremendous amount of recent activity is the field of generative modeling3–9. In this technique, a deep learning model is trained based on a corpus of existing molecules. The model typically ‘encodes’ a higher-dimensional representation, such as a SMILES (simplified molecular-input line-entry system)10, into a lower-dimensional representation, often referred to as a latent space. This latent space can then be ‘decoded’ back to the higherdimensional representation to create new molecules. The exploration of this latent space can be coupled with a predictive model with the aim of discovering novel, active molecules. In a sense, generative models can be seen as a variation on the de novo design11 programs that were in vogue during the 1990s and early 2000s. As with de novo design, evaluating the significance of the output of these models is not straightforward. Although two groups have made initial efforts at developing methods for benchmarking generative models12,13, evaluating the novelty, and ultimately the significance, of the molecules generated by these methods remains an open question. Such benchmarks provide a common ground for evaluation and comparison, but the ultimate value of generative models will be demonstrated through the synthesis and biological evaluation of the novel molecules they identify. One of the first examples of the synthesis and testing of molecules derived from a generative model was reported in a 2018 paper by Merk and co-workers14. In this paper, the authors began by training a generative model on a set of >500,000 bioactive molecules from the ChEMBL database. They then fine-tuned the model to generate molecules that would be agonists of retinoid X receptors (RXRs) or peroxisome proliferator‐activated receptors (PPARs). This fine-tuning process involved training based on a set of 25 fatty acid mimetics known to be agonists of PPAR or RXR. The molecules produced by the generative model were evaluated based on a quantitative structure–activity relationship (QSAR) model for PPAR and RXR activity. The authors then used the rankings from the QSAR model, along with a manual assessment of synthetic accessibility and chemical building block availability, to select five molecules to be synthesized. Although the authors reported that the five molecules selected for synthesis were not found in databases of reported molecules, they did not report the structures of the 25 fatty acid mimetics used in the training process. The five selected molecules were then synthesized, and two were found to be PPAR agonists with half-maximal effective concentration (EC50) values between 4 μM and 14 μM. Two additional compounds were dual PPAR and RXR inhibitors with EC50 values between 60 nM and 13 μM. The fifth compound was reported as inactive. Another, more recent example of the synthesis of a set of compounds based on a generative model comes from a paper by Zhavoronkov et al.15 published in the September issue of Nature Biotechnology. In this paper, the authors trained a generative model based on a large set of discoidin domain receptor family member 1 (DDR1) inhibitors extracted from the scientific and patent literature. Based on the output of the generative model, they synthesized six molecules. Of these six molecules, four had measurable biochemical activity, with the best, ‘compound 1’, having a 10 nM biochemical half-maximal inhibitory concentration (IC50). Compound 1 was then tested in the U2OS bone osteosarcoma cell line and shown to have an IC50 of 10.3 nM. In a subsequent mouse pharmacokinetic study, it was also shown to have reasonable bioavailability as well as a half-life of 3.5 h. This result received quite a bit of notice in the scientific and popular press and was hailed as ‘revolutionary’ by several pundits, some of them with clear competing financial interests16. An investor in the company that published the paper went as far as to refer to this as “Pharma’s AlphaGo Moment,” referring to the recent case in which Google’s program AlphaGo17 was able to defeat grandmaster Fan Hui in the challenging strategy game known as Go. One fact that seems to have escaped most of these pundits is the striking similarity between compound 1 in the Zhavoronkov paper and ponatinib (marketed as the drug Iclusig; Fig. 1). Ponatinib was originally developed by ARIAD Pharmaceuticals as a BCR-ABL inhibitor18 for the treatment of acute lymphocytic leukemia and chronic myelogenous leukemia. Subsequent testing showed that the compound was a potent inhibitor of multiple tyrosine kinases19. This broad kinase profile is believed to be Gao et al. Compound 7r 6 nM Zhavoronkov et al. Compound 1 10 nM Ponatinib 9 nM