Search Results for author: Yashaswi Pathak

Found 3 papers, 3 papers with code

Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning

1 code implementation • ICML 2020 • Sai Krishna Gottipati, Boris Sattarov, Sufeng. Niu, Hao-Ran Wei, Yashaswi Pathak, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, Sarath Chandar, Yoshua Bengio

In this work, we propose a novel reinforcement learning (RL) setup for drug discovery that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo compound design system.

Drug Discovery Navigate +3

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Code

Maximum Reward Formulation In Reinforcement Learning

1 code implementation • 8 Oct 2020 • Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Sahir, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E. Taylor, Sarath Chandar

Reinforcement learning (RL) algorithms typically deal with maximizing the expected cumulative return (discounted or undiscounted, finite or infinite horizon).

Drug Discovery reinforcement-learning +1

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Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

1 code implementation • 26 Apr 2020 • Sai Krishna Gottipati, Boris Sattarov, Sufeng. Niu, Yashaswi Pathak, Hao-Ran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio

Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.

Drug Discovery Navigate +2

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Code

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