Paper Title: FLEX: fixing flaky tests in machine learning projects by updating assertion bounds
Summary of DeepChem Usage: DeepChem unit tests are used to provide examples of flaky tests in ML software suites. This is an interesting use case of DeepChem for research that uses the raw source code and not the models/datasets themselves.
Date Published: August 23, 2021
Journal: ACM ESEC/FSE '21
DeepChem Papers and Discoveries List
Paper Title: Mol2vec: unsupervised machine learning approach with chemical intuition
Summary of DeepChem Usage: The Tox21 dataset is pulled down from MoleculeNet
Important Contributions: A new method for unsupervised learning on molecules is proposed.
Date Published: 2018
Journal: Journal of Chemical Information and Modeling
Paper Title: Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Summary of DeepChem Usage: The DeepChem graphconv implementation was used as a code reference and then converted to pytorch geometric. DeepChem featurizers were used as welll.
Important Contributions: A new method for improving graphconv explainability is developed.
Date Published: August 31, 2021
Journal: Arxiv
Paper Title: In silico prediction of drug‐induced ototoxicity using machine learning and deep learning methods
Summary of DeepChem Usage: DeepChem TextCNN and another model were used to benchmark methods.
Important Contributions: A high accuracy model for drug induced ototoxicity was developed
Date Published: May 19, 2021
Journal: Chemical Biology and Drug Design
Paper Title: ACED: Accelerated Computational Electrochemical systems Discovery
Summary of DeepChem Usage: ACED is working to build a julia port of DeepChem using DeepChem weave/featurizers as references
Important Contributions: ACED is working to make a large scale automated workflow for materials discovery
Date Published: November 23, 2020
Journal: Arxiv
Paper Title: Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction
Summary of DeepChem Usage: MoleculeNet featurizers are used to generate MPNN features
Important Contributions: A new attention and edge memory scheme for MPNNs is introduced.
Date Published: January 8, 2020
Journal: Journal of Cheminformatics
Paper Title: Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump
Summary of DeepChem Usage: DeepChem splitters and models were used
Important Contributions: A reliable model of drug induced liver injury is produced using the AMPL/DeepChem pipeline.
Date Published: February 28, 2020
Journal: Journal of Chemical Informatics and Modeling
Paper Title: Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
Summary of DeepChem Usage: DeepChem was used extensively to featurize data and build/optimize models.
Important Contributions: Graph convolutional networks are shown to be able to predict pharmacological activity against a broad range of targets.
Date Published: January 21, 2021
Journal: Nature Scientific Reports
Paper Title: Drugs repurposing using QSAR, docking and molecular dynamics for possible inhibitors of the SARS-CoV-2 Mpro protease
Summary of DeepChem Usage: DeepChem was used extensively to train models. Code is available at https://github.com/muntisa/Anticoronavirals-Classifier-using-DeepChem
Important Contributions: ML methods were used to investigate potential repurposed compounds for covid targeting Mpro
Date Published: 2020
Journal: Molecules
Paper Title: Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning
Summary of DeepChem Usage: The scaffold splitter, and ECFP fingerprints from DeepChem were used.
Important Contributions: A joint featurization method combining two standard molecular fingerprints is investigated.
Date Published: December 18, 2018
Journal: Frontiers in Pharmacology
Paper Title: A Self-Improving Photosensitizer Discovery System via Bayesian Optimization and Quantum Chemical Calculation
Summary of DeepChem Usage: Surrogate models were trained using DeepChem graphconvs.
Important Contributions: Using bayesian optimization plus graph convolutions, a high performance sensitizer is discovered.
Date Published: 2021
Journal: Chemrxiv
Paper Title: Investigating the Application of Interpretability Techniques to Computational Toxicology
Summary of DeepChem Usage: DeepChem is used extensively for datasets and modeling.
Important Contributions: Interpretabillity methods in computational toxicology are explored.
Date Published: 2021
Journal: UMD Honors Thesis
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Paper Title: Analyzing learned molecular representations for property prediction
Summary of DeepChem Usage: DeepChem/MoleculeNet models datasets are used to benchmark the new model.
Important Contributions: The D-MPNN model is one of the leading ML for chemistry models invented in the last couple of years.
Date Published: July 30, 2019
Journal: Journal of Chemical Informatics and Modeling
Paper Title: Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Summary of DeepChem Usage: DeepChem GraphConvs and MPNNs were used to baseline the new algorithms in this work.
Important Contributions: The new AttentiveFP model is introduced.
Date Published: August 13, 2019
Journal: Journal of Medicinal Chemistry
Paper Title: Potential neutralizing antibodies discovered for novel corona virus using machine learning
Summary of DeepChem Usage: DeepChem code is not directly used but the featurization code is adapted from DeepChem featurization code (compare https://github.com/BaratiLab/Potential-Neutralizing-antibodies-discovered-for-novel-corona-virus-using-machine-learning/blob/master/Ab_Virus.py#L45, https://github.com/deepchem/deepchem/blob/24607818b99c91f7864c18b249576b0c0dd3e06c/deepchem/feat/graph_features.py)
Important Contributions: Machine learning is used to identify potential neutralizing antibodies for covid using ML.
Date Published: March 4th, 2021
Journal: Nature Scientific Reports
Paper Title: GraphDTA: prediction of drug–target binding affinity using graph convolutional networks
Summary of DeepChem Usage: Atomic features for graphs are adapted from deepchem.
Important Contributions: Graph convolutional networks are used to predict drug/target interactions.
Date Published: October 2nd, 2020
Journal: Biorxiv
Paper Title: Padme: A deep learning-based framework for drug-target interaction prediction
Summary of DeepChem Usage: Weave featurizer and model adapted from DeepChem for their framework.
Important Contributions: PADME combines graph convolutions plus protein descriptors to make a protein-ligand interaction strength predictor.
Date Published: August 21st, 2019
Journal: Arxiv
Paper Title: Molecule attention transformer
Summary of DeepChem Usage: SVM, RF, Weave, and GraphConv models were used as baselines (from DeepCHem)
Important Contributions: Introduces the molecule attention transformer architecture.
Date Published: February 19th, 2020
Journal: Arxiv
Paper Title: Modeling physico-chemical ADMET endpoints with multitask graph convolutional networks
Summary of DeepChem Usage: Multitask graph convolutional networks are used directly.
Important Contributions: This paper generates that graph-convs are a powerful tool for modelling physico-chemical ADMET endpoints.
Date Published: December 1st, 2019
Journal: Molecules
Paper Title: Predicting or pretending: artificial intelligence for protein-ligand interactions lack of sufficiently large and unbiased datasets
Summary of DeepChem Usage: Atomic convolutions are used to train a protein ligand model from deepchem.
Important Contribution: Data biases in PDBBind and DUD-E are investigated using ACNNs.
Date Published: February 25th, 2020
Journal Published: Frontiers in Pharmacology