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Medicinal and Pharmaceutical Chemistry Commons™
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Full-Text Articles in Medicinal and Pharmaceutical Chemistry
Predicting Ffar4 Agonists Using Structure-Based Machine Learning Approach Based On Molecular Fingerprints, Zaid Anis Sherwani, Syeda Sumayya Tariq, Mamona Mushtaq, Ali Raza Siddiqui, Mohammad Nur-E-Alam, Aftab Ahmed, Zaheer Ul-Haq
Predicting Ffar4 Agonists Using Structure-Based Machine Learning Approach Based On Molecular Fingerprints, Zaid Anis Sherwani, Syeda Sumayya Tariq, Mamona Mushtaq, Ali Raza Siddiqui, Mohammad Nur-E-Alam, Aftab Ahmed, Zaheer Ul-Haq
Pharmacy Faculty Articles and Research
Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols …
De Novo Drug Design Using Transformer-Based Machine Translation And Reinforcement Learning Of An Adaptive Monte Carlo Tree Search, Dony Ang, Cyril Rakovski, Hagop S. Atamian
De Novo Drug Design Using Transformer-Based Machine Translation And Reinforcement Learning Of An Adaptive Monte Carlo Tree Search, Dony Ang, Cyril Rakovski, Hagop S. Atamian
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards …