Innovations in synthetic chemistry have enabled the discovery of many breakthrough therapies that have improved human health over the past century. In the face of increasing challenges in the pharmaceutical sector, continued innovation in chemistry is required to drive the discovery of the next wave of medicines. Novel synthetic methods not only unlock access to previously unattainable chemical matter, but also inspire new concepts as to how we design and build chemical matter. We identify some of the most important recent advances in synthetic chemistry as well as opportunities at the interface with partner disciplines that are poised to transform the practice of drug discovery and development.

We present the structure-based optimization of a series of estrogen receptor-beta (ERbeta) selective ligands. X-ray cocrystal structures of these ligands complexed to both ERalpha and ERbeta are described. We also discuss how molecular modeling was used to take advantage of subtle differences between the two binding cavities in order to optimize selectivity for ERbeta over ERalpha. Quantum chemical calculations are utilized to gain insight into the mechanism of selectivity enhancement. Despite only two relatively conservative residue substitutions in the ligand binding pocket, the most selective compounds have greater than 100-fold selectivity for ERbeta relative to ERalpha when measured using a competitive radioligand binding assay.

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

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By using a structure-based computer-assisted search, we have found a butyrophenone derivative that is a selective inhibitor of the human immunodeficiency virus 1 (HIV-1) protease. The computer program creates a negative image of the active site cavity using the crystal structure of the HIV-1 protease. This image was compared for steric complementarity with 10,000 molecules of the Cambridge Crystallographic Database. One of the most interesting candidates identified was bromperidol. Haloperidol, a closely related compound and known antipsychotic agent, was chosen for testing. Haloperidol inhibits the HIV-1 and HIV-2 proteases in a concentration-dependent fashion with a K1 of -100 ILM. It is highly selective, having little inhibitory effect on pepsin activity and no effect on renin at concentrations as high as 5 mM. The hydroxy derivative of haloperidol has a similar effect on HIV-1 protease but a lower potency against the HIV-2 enzyme. Both haloperidol and its hydroxy derivative showed activity against maturation of viral polypeptides in a cell assay system. Although this discovery holds promise for the generation of nonpeptide protease inhibitors, we caution that the serum concentrations of haloperidol in normal use as an antipsychotic agent are <10 ng/ml (0.03 IAM). Thus, concentrations required to inhibit the HIV-1 protease are >1000 times higher than the concentrations normally used. Haloperidol is highly toxic at elevated doses and can be life-threatening. Haloperidol is not useful as a treatment for AIDS but may be a useful lead compound for the development of an antiviral pharmaceutical.