ICML is one of the largest machine learning conferences and, like many other conferences this year, is running virtually from 12th – 18th July.
The list of accepted papers can be found here, with 1,088 papers accepted out of 4,990 submissions (22% acceptance rate). Similar to my post on NeurIPS 2019 papers, I will highlight several of potential interest to the chem-/bio-informatics communities. As before, given the large number of papers, these were selected either by “accident” (i.e. I stumbled across them in one way or another) or through a basic search (e.g. Ctrl+f “molecule”).
In addition to the various papers, there is also a computational biology workshop (workshop site)
I hope this list is fairly exhaustive, but no doubt I will have missed several. Please feel free to leave a comment and I will update the post accordingly. And now, without further ado, are the papers.
Title: Improving Molecular Design by Stochastic Iterative Target Augmentation
Authors: Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi Jaakkola
Preprint: https://arxiv.org/abs/2002.04720
Title: Hierarchical Generation of Molecular Graphs using Structural Motifs
Authors: Wengong Jin, Regina Barzilay, Tommi Jaakkola
Preprint: https://arxiv.org/abs/2002.03230
Title: Composing Molecules with Multiple Property Constraints
Authors: Wengong Jin, Regina Barzilay, Tommi Jaakkola
Preprint: https://arxiv.org/abs/2002.03244
Title: A Generative Model for Molecular Distance Geometry
Authors: Gregor Simm, Jose Miguel Hernandez-Lobato
Preprint: https://arxiv.org/abs/1909.11459
Title: Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Authors: Gregor Simm, Robert Pinsler, Jose Miguel Hernandez-Lobato
Preprint: https://arxiv.org/abs/2002.07717
Title: Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning
Authors: Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Haoran Wei, Yashaswi Pathak, Shengchao Liu, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, Sarath Chandar, Yoshua Bengio
Preprint: https://arxiv.org/abs/2004.12485
Title: Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Authors: Binghong Chen, Chengtao Li, Hanjun Dai, Le Song
Preprint: https://arxiv.org/abs/2006.15820
Title: A Graph to Graphs Framework for Retrosynthesis Prediction
Authors: Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang
Preprint: https://arxiv.org/abs/2003.12725
Title: Population-Based Black-Box Optimization for Biological Sequence Design
Authors: Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D. Sculley
Preprint: https://arxiv.org/abs/2006.03227
