NeurIPS is the largest machine learning conference (by number of participants), with over 8,000 in 2017. This year, the conference will be held in Vancouver, Canada from 8th-14th December.
Recently, the list of accepted papers was announced, with 1430 papers accepted. Here, I will highlight several of potential interest to the chem-/bio-informatics communities. 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”).
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: A Model to Search for Synthesizable Molecules
Authors: John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, José Miguel Hernández-Lobato
Preprint: https://arxiv.org/abs/1906.05221
Title: N-Gram Graph: A Simple Unsupervised Representation for Molecules
Authors: Shengchao Liu, Mehmet F Demirel, Yingyu Liang
Preprint: https://arxiv.org/abs/1806.09206
Title: Evaluating Protein Transfer Learning with TAPE
Authors: Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, Yun Song
Preprint: https://arxiv.org/abs/1906.08230
Title: Cormorant: Covariant Molecular Neural Networks
Authors: Brandon Anderson, Truong Son Hy, Risi Kondor
Preprint: https://arxiv.org/abs/1906.04015
Title: Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Authors: Niklas Gebauer, Michael Gastegger, Kristof Schütt
Preprint: https://arxiv.org/abs/1906.00957
Title: Deep imitation learning for molecular inverse problems
Authors: Eric Jonas
Preprint: N/A
Title: Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning
Authors: Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea
Preprint: N/A
Title: Retrosynthesis Prediction with Conditional Graph Logic Network
Authors: Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song
Preprint: N/A
Title: End-to-End Learning on 3D Protein Structure for Interface Prediction
Authors: Raphael Townshend, Rishi Bedi, Patricia Suriana, Ron Dror
Preprint: https://arxiv.org/abs/1807.01297
Title: Generative Models for Graph-Based Protein Design
Authors: John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola
Preprint: (ICLR 2019 workshop version) https://openreview.net/forum?id=SJgxrLLKOE
Title: A Stratified Approach to Robustness for Randomly Smoothed Classifiers
Authors: Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi Jaakkola
Preprint: https://arxiv.org/abs/1906.04948
