xinhaoli74 / aidm-2020-posters

Accepted Posters to present at the MIT's AI-powered Drug Discovery and Manufacturing Conference 2020.

AIDM-2020-Posters

Accepted Posters to present at the MIT's AI-powered Drug Discovery and Manufacturing Conference 2020. This repository contains the papers and codes (if published) of the 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

ChemRxiv | Github

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

arXiv | Github

Authors

Benjamin D. Madej

Generative Models for Graph-Based Protein Design

NeurIPS 2019 | Github

Authors

John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola

Transform drug discovery and development with the integration of AI, physics models, and computational resources: an experience 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

Github

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

Paper | Github

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

J. Chem. Inf. Model. | Github

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

ChemRxiv | Github

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

arXiv | Github

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

arXiv

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

Chemical Science | Github

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

Deep generative workflow for the real-world design of novel lead compounds

Authors

Sang Ok Song, Jae Hong Shin, Sanghyung Jin, Minkyu Ha, Jiho Yoo, Jinhan Kim

Increasing the applicability domain of QSAR models with meta-learning

Authors

Prudencio Tossou, Basile Dura

Interpretable graph neural networks for molecular property prediction

arXiv

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

ChemRxiv | Gitlab

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

Nature Communications | Github

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

arXiv

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, Steven F. Son

Deep Learning of Activation Energies

ChemRxiv

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

bioRxiv

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

arXiv

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

WebSite

Authors

Olaf Wiest, Nitesh Chawla, Abigail Doyle, Robert Paton, Richmond Sarpong, Matthew Sigman

Molecule Structure Elucidation given Mass Spectrum and Chemical Formula

Authors

David Jian Yi Lee, Bingquan Shen, Hai Leong Chieu

AI-assisted lead optimization with SynSpace

WebSite

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

Journal of Computer-Aided Molecular Design

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