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.
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).
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.