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.<\/p>\n\n\n\n
Recently, the list of accepted papers was announced<\/a>, 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”).<\/p>\n\n\n\n\n\n\n\n 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.<\/p>\n\n\n\n Title:<\/strong> A Model to Search for Synthesizable Molecules Title:<\/strong> N-Gram Graph: A Simple Unsupervised Representation for Molecules Title:<\/strong> Evaluating Protein Transfer Learning with TAPE Title:<\/strong> Cormorant: Covariant Molecular Neural Networks Title:<\/strong> Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Title:<\/strong> Deep imitation learning for molecular inverse problems Title:<\/strong> Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning Title:<\/strong> Retrosynthesis Prediction with Conditional Graph Logic Network Title:<\/strong> End-to-End Learning on 3D Protein Structure for Interface Prediction Title:<\/strong> Generative Models for Graph-Based Protein Design
Authors:<\/strong> John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, Jos\u00e9 Miguel Hern\u00e1ndez-Lobato
Preprint:<\/strong> https:\/\/arxiv.org\/abs\/1906.05221<\/a><\/p>\n\n\n\n
Authors:<\/strong> Shengchao Liu, Mehmet F Demirel, Yingyu Liang
Preprint:<\/strong> https:\/\/arxiv.org\/abs\/1806.09206<\/a><\/p>\n\n\n\n
Authors:<\/strong> Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, Yun Song
Preprint:<\/strong> https:\/\/arxiv.org\/abs\/1906.08230<\/a><\/p>\n\n\n\n
Authors:<\/strong> Brandon Anderson, Truong Son Hy, Risi Kondor
Preprint:<\/strong> https:\/\/arxiv.org\/abs\/1906.04015<\/a><\/p>\n\n\n\n
Authors:<\/strong> Niklas Gebauer, Michael Gastegger, Kristof Sch\u00fctt
Preprint:<\/strong> https:\/\/arxiv.org\/abs\/1906.00957<\/a><\/p>\n\n\n\n
Authors:<\/strong> Eric Jonas
Preprint:<\/strong> N\/A<\/p>\n\n\n\n
Authors:<\/strong> Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea
Preprint:<\/strong> N\/A<\/p>\n\n\n\n
Authors:<\/strong> Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song
Preprint:<\/strong> N\/A<\/p>\n\n\n\n
Authors:<\/strong> Raphael Townshend, Rishi Bedi, Patricia Suriana, Ron Dror
Preprint:<\/strong> https:\/\/arxiv.org\/abs\/1807.01297<\/a><\/p>\n\n\n\n
Authors:<\/strong> John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola
Preprint:<\/strong> (ICLR 2019 workshop version) https:\/\/openreview.net\/forum?id=SJgxrLLKOE<\/a><\/p>\n\n\n\n