Epigenetic drug discovery: systematic assessment of chemical space

The Acid/Base Characterization of Molecules with Epigenetic Activity

The acidic and basic functional groups in a molecule strongly influence its physicochemical properties, affinity for a macromolecule, pharmacokinetics, and toxicity. For instance, basicity has been correlated with molecular promiscuity, hERG blockade, and phospholipidosis. Nonetheless, no systematic characterization of the acid/base profile of epigenomic databases has been reported. This study describes an analysis of the acidic ionization constant distribution of a library of 7820 compounds with reported activity against epigenetic targets. Furthermore, the epigenomics database's acid/base profile was compared to the reference libraries of food chemicals, natural products, and approved drugs. It was found that the acid/base profile of histone lysine demethylase ligands is more similar to previously approved drugs, and histone acetyltransferase ligands have acidic and basic functional groups largely found in food chemicals and natural products; this support the potential of these libraries for finding new epigenetic inhibitors.

Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2

The current life-threatening and tenacious pandemic eruption of coronavirus disease in 2019 (COVID-19) has posed a significant global hazard concerning high mortality rate, economic meltdown, and everyday life distress. The rapid spread of COVID-19 demands countermeasures to combat this deadly virus. Currently, there are no drugs approved by the FDA to treat COVID-19. Therefore, discovering small molecule therapeutics for treating COVID-19 infection is essential. So far, only a few small molecule inhibitors are reported for coronaviruses. There is a need to expand the small chemical space of coronaviruses inhibitors by adding potent and selective scaffolds with anti-COVID activity. In this context, the huge antiviral chemical space already available can be analysed using cheminformatic and machine learning to unearth new scaffolds. We created three specific datasets called "antiviral dataset" (N = 38,428) "drug-like antiviral dataset" (N = 20,963) and "anticorona dataset" (N = 433) for this purpose. We analyzed the 433 molecules of "anticorona dataset" for their scaffold diversity, physicochemical distributions, principal component analysis, activity cliffs, R-group decomposition, and scaffold mapping. The scaffold diversity of the "anticorona dataset" in terms of Murcko scaffold analysis demonstrates a thorough representation of diverse chemical scaffolds. However, physicochemical descriptor analysis and principal component analysis demonstrated negligible drug-like features for the "anticorona dataset" molecules. The "antiviral dataset" and "drug-like antiviral dataset" showed low scaffold diversity as measured by the Gini coefficient. The hierarchical clustering of the "antiviral dataset" against the "anticorona dataset" demonstrated little molecular similarity. We generated a library of frequent fragments and polypharmacological ligands targeting various essential viral proteins such as main protease, helicase, papain-like protease, and replicase polyprotein 1ab. Further structural and chemical features of the "anticorona dataset" were compared with SARS-CoV-2 repurposed drugs, FDA-approved drugs, natural products, and drugs currently in clinical trials. Using machine learning tool DCA (DMax Chemistry Assistant), we converted the "anticorona dataset" into an elegant hypothesis with significant functional biological relevance. Machine learning analysis uncovered that FDA approved drugs, Tizanidine HCl, Cefazolin, Raltegravir, Azilsartan, Acalabrutinib, Luliconazole, Sitagliptin, Meloxicam (Mobic), Succinyl sulfathiazole, Fluconazole, and Pranlukast could be repurposed as effective drugs for COVID-19. Fragment-based scaffold analysis and R-group decomposition uncovered pyrrolidine and the indole molecular scaffolds as the potent fragments for designing and synthesizing the novel drug-like molecules for targeting SARS-CoV-2. This comprehensive and systematic assessment of small-molecule viral therapeutics' entire chemical space realised critical insights to potentially privileged scaffolds that could aid in enrichment and rapid discovery of efficacious antiviral drugs for COVID-19.

Recent progress on cheminformatics approaches to epigenetic drug discovery

The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.