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Molecular dynamics simulation with GFlowNets: machine learning the importance of energy estimators in computational chemistry and drug discovery
Manage episode 442916985 series 2462838
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
Become a patron of Breaking Math for as little as a buck a month
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.
Follow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTok
Follow Autumn on Twitter and Instagram
Follow Gabe on Twitter.
Become a guest here
email: breakingmathpodcast@gmail.com
145 episoade
Manage episode 442916985 series 2462838
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
Become a patron of Breaking Math for as little as a buck a month
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.
Follow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTok
Follow Autumn on Twitter and Instagram
Follow Gabe on Twitter.
Become a guest here
email: breakingmathpodcast@gmail.com
145 episoade
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