Search Results for author:
Found 13 papers, 4 papers with code
Towards understanding retrosynthesis by energy-based models
In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions.
Improving Hit-finding: Multilabel Neural Architecture with DEL
DNA-Encoded Libraries (DEL thereafter) data, often with millions of data points, enables large deep learning models to make real contributions in the drug discovery process (e. g., hit-finding).
Pursuing a Prospective Perspective
Retrospective testing of predictive models does not consider the real-world context in which models are deployed.
Energy-based View of Retrosynthesis
Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery.
Ranked #1 on
Single-step retrosynthesis
on USPTO-50k
Machine learning on DNA-encoded libraries: A new paradigm for hit-finding
We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collection and a virtual library of easily synthesizable compounds.
Decoding Molecular Graph Embeddings with Reinforcement Learning
We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings.
Optimization of Molecules via Deep Reinforcement Learning
We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning and randomized value functions).
Ranked #1 on
Molecular Graph Generation
on ZINC
(QED Top-3 metric)
Molecular Graph Generation
Multi-Objective Reinforcement Learning
+2
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer.
Machine learning prediction errors better than DFT accuracy
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules.
Ranked #18 on
Formation Energy
on QM9
Modeling Industrial ADMET Data with Multitask Networks
Deep learning methods such as multitask neural networks have recently been applied to ligand-based virtual screening and other drug discovery applications.
ROCS-Derived Features for Virtual Screening
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity.
Molecular Graph Convolutions: Moving Beyond Fingerprints
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications.
Ranked #4 on
Graph Regression
on Lipophilicity
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources.
