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Molecular dynamics simulation with GFlowNets: machine learning the importance of energy estimators in computational chemistry and drug discovery
- 2024/10/01
- 再生時間： 28 分
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Molecular dynamics simulation with GFlowNets: machine learning the importance of energy estimators in computational chemistry and drug discovery
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あらすじ・解説
This episode, Breaking Math does a deep dive of “Towards equilibrium molecular conformation generation with GFlowNets” by Volokova et al in Digital Discovery Journal by the Royal Society of Chemistry. Hosts Autumn and Gabriel explore the intersection of molecular conformations and machine learning. They discuss traditional methods like molecular dynamics and cheminformatics, and introduce generative flow networks (GFlowNets) as a revolutionary approach to molecular confirmation generation. The conversation highlights empirical results demonstrating the effectiveness of GFlowNets, their scalability, and the importance of energy estimators in computational chemistry and drug discovery.
Keywords: molecular conformations, machine learning, GFlowNets, computational chemistry, drug discovery, molecular dynamics, cheminformatics, energy estimators, empirical results, scalability, math, mathematics, physics, AI
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You can find the paper “Towards equilibrium molecular conformation generation with GFlowNets” by Volokova et al in Digital Discovery Journal by the Royal Society of Chemistry.
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