AI Powered Drug Discovery and Manufacturing
CONFERENCE 2020
February 27 - 28, 2020
MIT, Cambridge, MA
Posters
Evaluating the performance of in-house, commercial, and open models for early ADMET properties
Authors
Renee DesJarlais, Kiran Kumar, Vladimir Chupakhin, Hugo Ceulemans, Dmitrii Rassokhin
Target Deconvolution by Matching Gene and Drug Perturbations
Authors
Robin Luo, Yuning Bie
Using Machine Learning vs Molecular Similarity in Predicting Enzymatic Steps in Biochemical Synthesis Routes
Authors
Gian Marco Visani, Michael C. Hughes, Soha Hassoun
A reaction inspector for identifying impurities in organic reactions
Authors
Yiming Mo, Hanyu Gao, Klavs F. Jensen
Deep Learning reveals 3D atherosclerotic plaque distribution and composition
Authors
Vanessa Isabell Jurtz, Grethe Skovbjerg, Casper Gravesen Salinas, Urmas Roostalu, Louise Pedersen, Bidda Charlotte Rolin, Michael Nyberg, Martijn van de Bunt, Camilla Ingvorsen
Enzymatic link prediction for biochemical route synthesis
Authors
Julie Jiang, Li-Ping Liu, Soha Hassoun
Integrating Deep Neural Networks and Symbolic Inference for Organic Reactivity Prediction
Authors
Wesley Wei Qian, Nathan Russell, Claire L. W. Simons, Yunan Luo, Martin D. Burke, Jian Peng
Machine Learning guided peptide design in a drug discovery context
Authors
Simone Fulle, Carsten Stahlhut, Søren Berg Padkjær
Synthetic Planning and Library Optimization for Automated Cross Coupling Synthesis Platforms
Authors
Nathan Russell, Andrea Palazzolo, Claire Simmons, Martin Burke, Jian Peng
Learning Neural Retrosynthetic Planning
Authors
Binghong Chen, Hanjun Dai, Chengtao Li, Le Song
Explicit geometric information in molecular property prediction
Authors
Martin Vögele, Ron Dror
Applications of the ATOM Modeling PipeLine (AMPL) for Pharmacokinetics Property Prediction
Authors
Benjamin D. Madej
Generative Models for Graph-Based Protein Design
Authors
John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola
How to transform drug discovery and development using Artificial Intelligence and physics-based models? An integrated computational approach from XtalPi
Authors
Sivakumar Sekharan, Lipeng Lai, et al.
Prediction of IR Spectra with Machine Learning
Authors
Michael Forsuelo, Charles McGill, Yanfei Guan, William Green
Learning a molecular latent space organized by binding affinities
Authors
Jacques Boitreaud, Carlos Oliver, Vincent Mallet, Jerome Waldispühl
Rational Design of a Parallel Synthesis Program for the Optimization of Anti-fungal HDAC Inhibitors
Authors
Benjamin Merget, B. Merget, C. Wiebe, A. Koch
ChemBERT: A pretrained language model for the extraction of chemical reaction information
Authors
Jiang Guo, A.S. Ibanez-Lopez, Victor Quach, Regina Barzilay
Predicting the impact of somatic mutations using cell painting
Authors
Juan C. Caicedo, Shantanu Singh, Alice Berger, Angela Brooks, Jesse Boehm, Anne E. Carpenter
Predicting Drug Activity using Combined Phenotypic Features and Chemical Structures
Authors
Tim Becker, Kevin Yang, Juan Caicedo, Shantanu Singh, Tommi Jaakkola, Regina Barzilay, Anne E. Carpenter
Learning mass spectrometry fragmentation of small molecules
Authors
Liu Cao, Alexey Gurevich, Hosein Mohimani
LigandNet: A machine learning toolbox for predicting ligand activity towards therapeutically important proteins
Authors
Md Mahmudulla Hassan, Danielle Castaneda-Mogollon, Govinda KC, and Suman Sirimulla
Deep Learning-Generated Potential NMDA Receptor Antagonists Using a Variational Autoencoder
Authors
Katherine J. Schultz, Sean M. Colby, Yasemin Yesiltepe, Jamie R. Nuñez, Monee Y. McGrady, Ryan R. Renslow
Predicting Drug Activity using Combined Phenotypic Features and Chemical Structures
Authors
Tim Becker, Kevin Yang, Juan Caicedo, Shantanu Singh, Regina Barzilay, Anne E. Carpenter
CORE: Automatic Molecule Optimization Using Copy and Refine Strategy
Authors
Tianfan Fu, Cao Xiao, Jimeng Sun
Regioselectivity Predictions Using Graph Network and Quantum Descriptors
Authors
Yanfei Guan, Thomas Struble, Oscar Wu, Lagnajit Pattanaik, Connor Coley, William H. Green, Klavs F. Jensen
Property Prediction for 2-Molecule Mixtures
Authors
Allison Tam, Octavian Ganea, Gary Becigneul, Regina Barzilay
Optimizing Synthesis Plans for Molecular Libraries
Authors
Hanyu Gao, Jean Pauphilet, Thomas J. Struble, Connor W. Coley, William H. Green, Klavs F. Jensen
Analyzing Learned Molecular Representations for Property Prediction
Authors
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay
Machine Learning for Ligand Design in Palladium-Catalyzed Cross Coupling Reactions
Authors
Jessica Xu, Thomas Struble, Yanfei Guan, Priscilla Liow, Joseph M. Dennis, Stephen L. Buchwald, and Klavs F. Jensen
Practical constraints on machine learning in drug discovery - a case study
Authors
Dominique Beaini, Lu Zhu, Daniel Cohen, Sébastien Giguère
Thermodynamic Properties of Materials: Towards the Prediction of Solubility
Authors
Florence H. Vermeire, William H. Green
Wasserstein Graph Representation and Generation
Authors
Gary Bécigneul, Benson Chen, Octavian Ganea, Tommi Jaakkola, Regina Barzilay
Drug-BERT: Pre-training Drug Sub-structure Representation for Molecular Property Prediction
Authors
Kexin Huang, Cao Xiao, Lucas M. Glass, Jimeng Sun
3D Molecular Representation and Modeling using Deep Learning
Authors
Tomohide Masuda, Matthew Ragoza, David Ryan Koes
Inductive Transfer Learning for Molecular Activity Prediction
Authors
Xinhao Li, Denis Fourches
Learning accurate generative models of chemical structures from limited training examples
Authors
Michael A. Skinnider, Leonard J. Foster
Hierarchical Graph-to-Graph Translation for Molecules
Authors
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Practical Application of Deep Learning to Drug Discovery Project Data
Authors
Thomas Whitehead, Ben Irwin, Julian Levell, Matthew Segall, Gareth Conduit
Analyzing uncertainty estimates for deep molecular property prediction
Authors
Gabriele Scalia, Colin Grambow, Barbara Pernici, Drew Wicke, Vladimir Chupakhin, Hugo Ceulemans, William H. Green
A deep learning approach to antibiotic discovery
Authors
Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackerman, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, Tommi S. Jaakkola, Regina Barzilay, and James J. Collins
Application of Multi-Armed Bandit Problems to Ensemble FMO to Efficiently Propose Promising Idea Compounds
Authors
Kenichiro Takaba
Performance of Reaction Datasets in Machine Learning Approaches to Synthesis Planning and Use in Restricted Domains
Authors
Amol Thakkar, Jean-Louis Reymond, Ola Engkvist, Esben Jannik Bjerrum
Transfer Learning: A Next Key Driver of Accelerating Materials Discovery with Machine Learning
Authors
Chang Liu, Hironao Yamada, Stephen Wu, Yokinori Koyama, Ryo Yoshida
Uncertainty Quantification in Molecular Property Prediction using Message Passing Networks
Authors
Lior S Hirschfeld, Kyle Swanson, Regina Barzilay, and Connor Coley
Interpretable graph neural networks for molecular property prediction
Authors
Emmanuel Noutahi, Dominique Beani, Julien Horwood, Prudencio Tossou
Machine Learning in Quantum Chemistry: Art or Science?
Authors
Anton V. Sinitskiy, Daniil V. Izmodenov, Iosif V. Leibin, Georgiy K. Ozerov, Dmitry S. Bezrukov, Vijay S. Pande
Fully Convolutional Neural Network Models for Materials Science Applications
Authors
Abraham Stern, Michelle Gill, Dave Magley, Ellen Du, Ryan Marson, Jonathan Moore, Bart Rijksen, Joey Storer, Sukrit Mukhopadhyay
Data Augmentation and Pre-training for Template-Based Retrosynthetic Prediction
Authors
Michael E Fortunato, Connor W Coley, Brian C Barnes, Klavs F Jensen
Efficient Modeling of Reaction Outcomes Using Active Machine Learning
Authors
Natalie S. Eyke, William H. Green, Klavs F. Jensen
Reaction Condition Prediction
Authors
Jiannan Liu, Hanyu Gao, Thomas Struble, Klavs F. Jensen
Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
Authors
Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey Grossman
Generative Molecular Design for Lead Optimization: Demonstration by Discovery of Potent, Selective Aurora Kinase B Inhibitors with Favorable Candidate Quality Attributes
Authors
Andrew D Weber, Jason Z Deng, Kevin McLoughlin, Jeffrey Mast, Thomas Sweitzer, Juliet McComas, Margaret Tse, Derek Jones, Jonathan Allen, Stacie Calad-Thomson, Jim Brase, Tom Rush
Black Box Recursive Translations for Molecular Optimization
Authors
Farhan Damani, Vishnu Sresht, Stephen Ra
Leveraging non-structural data to predict structures of protein–ligand complexes
Authors
Joseph Paggi, Ron Dror
Use of Full 3D Electronic Structure with a Convolutional Neural Network for Prediction of Molecular and Energetic Material Properties
Authors
Brian C. Barnes, Alex D. Casey, Betsy M. Rice, Ilias Bilionis, Steven F. Son
Leveraging non-structural data to predict structures of protein–ligand complexes
Authors
Joseph M. Paggi, Ron O. Dror
Deep Learning of Activation Energies
Authors
Colin A. Grambow, Lagnajit Pattanaik, William H. Green
Encoding periodic trends for fully transferable neural network potentials
Authors
John E. Herr, Kevin Koh, Kun Yao, John Parkhill
Olympus: A Toolkit for Benchmarking Optimization Algorithms on Experimentally Derived Surfaces
Authors
F. Häse, R.J. Hickman, M. Aldeghi, E. Liles, L.M. Roch, J.E. Hein, A. Aspuru-Guzik
Smart Data Analytics in Pharmaceutical Manufacturing
Authors
Weike Sun, Kristen A. Severson, Richard D. Braatz
Generating 3D transition state structures with deep learning
Authors
Lagnajit Pattanaik, John Ingraham, Colin Grambow, Regina Barzilay, Tommi Jaakkola, Klavs Jensen, William Green
Document embedding centroids: new and versatile descriptors for biomedical entities
Authors
John P. Santa Maria Jr., Eugen Lounkine, Jeremy Jenkins
Predicting therapeutic targets of small molecules using an integrated machine learning framework
Authors
Nishanth Merwin, Dr. Victoria Catterson, Dr. Gabriel Musso
Finding diverse routes of organic synthesis using surrogate-accelerated Bayesian retrosynthesis
Authors
Zhongliang Guo, Stephen Wu, Ryo Yoshida
Augmenting protein network embeddings with sequence information
Authors
Hassan Kané, Mohamed Coulibali, Pelkins Ajanoh, Ali Abdalla
Learning Representations of DNA Sequences for Low Resource Promoter Characterization
Authors
Peter Morales, Rajmonda Caceres, Matt Walsh, Christina Zook, Catherine Van Praagh, Nicholas Guido, Todd Thorsen
Antibody’s developability prediction with machine learning and a high quality data set
Authors
Lei Jia, Yax Sun, Alex Jacobitz, Vladimir Razinkov, Marissa Mock, Nic Angell, Peter Grandsard, BAT team
Benchmarking the Synthesizability of Molecules Proposed via de novo Generative Models
Authors
Wenhao Gao, Connor Coley, Klavs Jensen
Using big data and machine learning to understand T cell dysfunction in human tumors
Authors
Simarjot Pabla, Tenzing Khendu, Cailin Joyce, Benjamin Duckless, Andrew Basinski, Matthew Hancock, Jeremy Waight, Mariana Manrique, Jennifer Buell, Alex Duncan, David Savitsky, Lukasz Swiech, Thomas Horn, John Castle
PostEra - The GPS for Chemistry
Authors
Alpha Lee, Matthew Robinson, Aaron Morris
The Center for Computer-Assisted Synthesis: An Overview
Authors
Olaf Wiest, Nitesh Chawla, Abigail Doyle, Robert Paton, Richmond Sarpong, Matthew Sigman
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Authors
Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato
AI-assisted lead optimization with SynSpace
Authors
Istvan Szabo, Greg Makara, Gabor Pocze, Laszlo Kovacs, Anna Szekely
Machine learning guided process development for complex systems with multiple objectives
Authors
Perman Jorayev, Danilo Russo, Paul Deutsch, Alexei Lapkin
Applying state-of-art deep learning models to design novel JAK inhibitors
Authors
Vykintas Jauniškis, Ole Winther, Daniel R. Greve
Exploring Fragment-based Target-specific Ranking Protocol with Machine Learning on Cathepsin S
Authors
Yuwei Yang
Optical Graph Recognition of Chemical Compounds by Deep Learning
Authors
Martijn Oldenhof, Adam Arany, Yves Moreau, Jaak Simm
Computational Pipeline to Discover Inhibitors of RPN11 using Machine Learning and Image Processing
Authors
Aayush Gupta, Huan-Xiang Zhou
Accelerating lead optimization with active learning by exploiting MMPA based ADMET knowledge with regression forest potency models
Authors
Alexander. G. Dossetter, Edward J. Griffen, Andrew G. Leach, Phillip de Sousa
AI Driven Iterative Screening for Hit Finding
Authors
Gabriel Dreiman, Magda Bictash, Paul Fish, Lewis Griffin, Fredrik Svensson
Applications of Machine Learning in Modeling Small-Molecule Structures, Energies, and Reactions
Authors
Cyndi He, Colin Lam, Kavindri Ranasinghe, Ed Sherer, Marion H. Emmert, Harry Chobanian
Comparison of Deep Learning Methods in ADMET Prediction and Application in Lead Optimization
Authors
Song Yang, Jonathan Tynan, Alan C. Cheng
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Authors
Benjamin Sanchez-Lengeling, Jennifer N Wei, Brian K Lee, Richard C Gerkin, Alán Aspuru-Guzik, and Alexander B Wiltschko
Improving Molecular Design by Stochastic Iterative Target Augmentation
Authors
Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay and Tommi Jaakkola