A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds

HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds are in great need. By targeting the major reverse transcription residues Y181, K103, and L100, we used the biological activities of compounds against these enzymes and the wild-type reverse transcriptase to create Naïve Bayes Networks. Through this machine learning approach, we could predict, with high accuracy, whether a compound would be susceptible to a loss of potency due to resistance. Also, we could perfectly predict retrospectively whether compounds would be susceptible to both a K103 mutant RT and a Y181 mutant RT. In the study presented here, our method outperformed a traditional molecular mechanics approach. This method should be of broad interest beyond drug discovery efforts, and serves to expand the utility of machine learning for the prediction of physical, chemical, or biological properties using the vast information available in the literature

A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds

HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds are in great need. By targeting the major reverse transcription residues Y181, K103, and L100, we used the biological activities of compounds against these enzymes and the wild-type reverse transcriptase to create Naïve Bayes Networks. Through this machine learning approach, we could predict, with high accuracy, whether a compound would be susceptible to a loss of potency due to resistance. Also, we could perfectly predict retrospectively whether compounds would be susceptible to both a K103 mutant RT and a Y181 mutant RT. In the study presented here, our method outperformed a traditional molecular mechanics approach. This method should be of broad interest beyond drug discovery efforts, and serves to expand the utility of machine learning for the prediction of physical, chemical, or biological properties using the vast information available in the literature

Novel Cyclopropyl-Indole Derivatives as HIV Non-Nucleoside Reverse Transcriptase Inhibitors

The HIV pandemic represents one of the most serious diseases to face mankind in both a social and economic context, with many developing nations being the worst afflicted. Due to ongoing resistance issues associated with the disease, the design and synthesis of anti-HIV agents presents a constant challenge for medicinal chemists. Utilizing molecular modeling, we have designed a series of novel cyclopropyl indole derivatives as HIV non-nucleoside reverse transcriptase inhibitors and carried out their preparation. These compounds facilitate a double hydrogen bonding interaction to Lys101 and efficiently occupy the hydrophobic pockets in the regions of Tyr181/188 and Val179. Several of these compounds inhibited HIV replication as effectively as nevirapine when tested in a phenotypic assay

Novel Microtubule-Targeting 7‑Deazahypoxanthines Derived from Marine Alkaloid Rigidins with Potent in Vitro and in Vivo Anticancer Activities

Docking studies of tubulin-targeting C2-substituted 7-deazahypoxanthine analogues of marine alkaloid rigidins led to the design and synthesis of compounds containing linear C2-substituents. The C2-alkynyl analogue was found to have double- to single-digit nanomolar antiproliferative IC₅₀ values and showed statistically significant tumor size reduction in a colon cancer mouse model at nontoxic concentrations. These results provide impetus and further guidance for the development of these rigidin analogues as anticancer agents

Design and Optimization of Novel Competitive, Non-peptidic, SARS-CoV‑2 M^pro Inhibitors

The SARS-CoV-2 main protease (Mpro) has been proven to be a highly effective target for therapeutic intervention, yet only one drug currently holds FDA approval status for this target. We were inspired by a series of publications emanating from the Jorgensen and Anderson groups describing the design of potent, non-peptidic, competitive SARS-CoV-2 Mpro inhibitors, and we saw an opportunity to make several design modifications to improve the overall pharmacokinetic profile of these compounds without losing potency. To this end, we created a focused virtual library using reaction-based enumeration tools in the Schrödinger suite. These compounds were docked into the Mpro active site and subsequently prioritized for synthesis based upon relative binding affinity values calculated by FEP+. Fourteen compounds were selected, synthesized, and evaluated both biochemically and in cell culture. Several of the synthesized compounds proved to be potent, competitive Mpro inhibitors with improved metabolic stability profiles