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Found 10 papers, 7 papers with code
Evolving symbolic density functionals
Systematic development of accurate density functionals has been a decades-long challenge for scientists.
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures.
Scaling Symbolic Methods using Gradients for Neural Model Explanation
In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation.
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.
Neural-Guided Symbolic Regression with Asymptotic Constraints
The second part, which we call Neural-Guided Monte Carlo Tree Search, uses the network during a search to find an expression that conforms to a set of data points and desired leading powers.
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.
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.
