Timezone: »
Artificial intelligence has not yet revolutionized the design of materials and molecules. In this perspective, we identify four barriers preventing the integration of atomistic deep learning, molecular science, and high-performance computing. We outline focused research efforts to address the opportunities presented by these challenges.
Author Information
Nathan Frey (Massachusetts Institute of Technology)
I am a theoretical materials physicist, National Defense Science & Engineering Graduate fellow, and PhD candidate in Materials Science & Engineering at the University of Pennsylvania in Philadelphia. I use multiscale modeling and computational techniques, including machine learning, to discover and design new materials for next-generation information processing platforms and sustainable energy applications.
Siddharth Samsi (MIT Lincoln Laboratory)
Bharath Ramsundar (DeepChem)
Connor Coley (MIT)
More from the Same Authors
-
2021 : Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development »
Kexin Huang · Tianfan Fu · Wenhao Gao · Yue Zhao · Yusuf Roohani · Jure Leskovec · Connor Coley · Cao Xiao · Jimeng Sun · Marinka Zitnik -
2021 Spotlight: GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles »
Octavian Ganea · Lagnajit Pattanaik · Connor Coley · Regina Barzilay · Klavs Jensen · William Green · Tommi Jaakkola -
2021 : Scalable Geometric Deep Learning on Molecular Graphs »
Nathan Frey · Siddharth Samsi · Lin Li · Connor Coley -
2022 : De novo PROTAC design using graph-based deep generative models »
Divya Nori · Connor Coley · Rocío Mercado -
2022 : De novo PROTAC design using graph-based deep generative models »
Divya Nori · Connor Coley · Rocío Mercado -
2023 Poster: Prefix-tree decoding for predicting mass spectra from molecules »
Samuel Goldman · John Bradshaw · Jiayi Xin · Connor Coley -
2022 : A High-Throughput Platform for Efficient Exploration of Polypeptides Chemical Space via Automation and Machine Learning »
Guangqi Wu · Connor Coley · Hua Lu -
2022 : Automated Materials Synthesis Keynote »
Connor Coley -
2022 : MolPAL: Software for Sample Efficient High-Throughput Virtual Screening »
David Graff · Connor Coley -
2022 Poster: Reinforced Genetic Algorithm for Structure-based Drug Design »
Tianfan Fu · Wenhao Gao · Connor Coley · Jimeng Sun -
2022 Poster: Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization »
Wenhao Gao · Tianfan Fu · Jimeng Sun · Connor Coley -
2021 : AI X Chemistry »
Connor Coley -
2021 : Live Panel »
Max Welling · Bharath Ramsundar · Irina Rish · Karianne J Bergen · Pushmeet Kohli -
2021 Poster: Learning Graph Models for Retrosynthesis Prediction »
Vignesh Ram Somnath · Charlotte Bunne · Connor Coley · Andreas Krause · Regina Barzilay -
2021 Poster: GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles »
Octavian Ganea · Lagnajit Pattanaik · Connor Coley · Regina Barzilay · Klavs Jensen · William Green · Tommi Jaakkola -
2020 Poster: SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology »
Mark Veillette · Siddharth Samsi · Chris Mattioli -
2019 Poster: Retrosynthesis Prediction with Conditional Graph Logic Network »
Hanjun Dai · Chengtao Li · Connor Coley · Bo Dai · Le Song -
2017 Poster: Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network »
Wengong Jin · Connor Coley · Regina Barzilay · Tommi Jaakkola