Multi Author Pharmacology Pdf Download BETTER

Drug discovery has been traditionally performed by computer programs that use various descriptors to design molecules, but these programs are not able to generate diverse molecules. The author group therefore designed and implemented a novel method to generate diverse molecules using reinforcement learning (RL). RL is a learning technique that enables a computer agent to learn by trial and error. Using RL, we developed a method to generate diverse molecules with a desired selectivity profile for a given target, which can be used as a complement to existing methods. We used the comparative molecular field analysis (CoMFA) to describe the pharmacophore of the A1 adenosine receptor (A1AR) and compared the fit of generated molecules with the top-ranked molecules in the LIGAND set. We also performed molecular docking to compare the binding affinity of generated molecules with the top-ranked molecules in the LIGAND set. We then developed a genetic algorithm (GA)-based RL framework to design molecules with the desired selectivity profile. We tested this RL framework using both multi-target and target-specific cases. These cases were designed by applying different criteria, with different levels of selectivity towards the A1AR and A2AAR, respectively. For the multi-target case, we required the generated molecules to have a high affinity towards both A1AR and A2AAR.

The first two authors (J.G. and K.K.) conceived the research and contributed equally to the writing of the manuscript. K.K. designed and programmed the software. H.Y., M.K. and S.Y. performed the experiments.

In this study we have used this checklist to evaluate the outputs generated by DrugEx v2 with PF scheme on both multi-target and target-specific case. We hope that this effort will help authors to improve the reproducibility of their publications and enhance transparency and accountability of the results.

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