首页|University of California Reports Findings in Artificial Intelligence (Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Method ology for Ligand Based and Structure Based Drug Design)
University of California Reports Findings in Artificial Intelligence (Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Method ology for Ligand Based and Structure Based Drug Design)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Berkeley, Califor nia, by NewsRx journalists, research stated, "Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with realtime 3D molecular do cking using AutoDock Vina, thereby simultaneously creating chemical novelty whil e constraining molecules for shape and molecular compatibility with target activ e sites." The news correspondents obtained a quote from the research from the University o f California, "Moreover, through the use of various types of reward functions, w e have introduced novelty in generative tasks for new molecules such as chemical similarity to a target ligand, molecules grown from known protein bound fragmen ts, and creation of molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflo w that filters out Pan-assay interference compounds, Lipinski rule violations, u ncommon structures in medicinal chemistry, and poor synthetic accessibility with options for cross-validation against other docking scoring functions and automa tion of a molecular dynamics simulation to measure pose stability. We also allow users to define a set of rules for the structures they would like to exclude du ring the training process and postfiltering steps."
BerkeleyCaliforniaUnited StatesNor th and Central AmericaArtificial IntelligenceDrug DevelopmentDrugs and The rapiesEmerging TechnologiesHealth and MedicineMachine Learning
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