A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
DOCKSTRING simplifies docking simulations for better drug design benchmarks.
Improved RL model for fragment-based molecule generation.
CNP improves few-shot learning for docking scores in molecular datasets.
Benchmark proposes to assess molecule docking efficiency.
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph. In the 3DGCN, graph convolution is unified with learning operations on the vector to handle the spatial information from molecular topology. The 3DGCN model ex…
Motivation: Prediction of ligands for proteins of known 3D structure is important to understand structure-function relationship, predict molecular function, or design new drugs. Results: We explore a new approach for ligand prediction in which binding pockets are represented by atom clouds. Each target pocket is compar…
New model uses pretrained biochemical language models to generate drug compounds.
Motivation: Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is no docking method available for rigorous uncertainty quantification (UQ) of its solution quality (e.g. interface RMSD or iR…
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that include…
Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug discovery goal. The use of deep learning technologies could augment the …
In recent years, dock-less shared bikes have been widely spread across many cities in China and facilitate people's lives. However, at the same time, it also raises many problems about dock-less shared bike management due to the mismatching between demands and real distribution of bikes. Before deploying dock-less shar…
Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowled…
Docking is an important tool in computational drug discovery that aims to predict the binding pose of a ligand to a target protein through a combination of pose scoring and optimization. A scoring function that is differentiable with respect to atom positions can be used for both scoring and gradient-based optimization…
Omics-GAN uses GANs to generate synthetic multi-omics data for improved disease prediction.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
A new model designs molecular latent vectors for drug discovery.
MoFlow generates chemically valid molecular graphs from latent representations.
Study compares GNNs and classical molecular featurisations for molecular property and cliff prediction.
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules w…
Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervised predictive models in the downstream tas…
LSS learns molecular trajectories from MD data.
AniDS improves molecular force field modeling by learning anisotropic noise.
Machine learning models simulate molecular spectra and reactions in solvents.
Framework for training-free guidance in discrete diffusion models for molecular generation.
Optimizes molecular generation for chemist preferences.
New RL method designs 3D molecules with improved symmetry.
Generative models accelerate molecular dynamics by four orders of magnitude.
FlowMO uses Gaussian Processes for molecular property prediction with uncertainty.
Automating molecular design using deep reinforcement learning (RL) holds the promise of accelerating the discovery of new chemical compounds. Existing approaches work with molecular graphs and thus ignore the location of atoms in space, which restricts them to 1) generating single organic molecules and 2) heuristic rew…
Framework learns surrogates for molecular dynamics across multiple time-scales.
Paper improves molecular property prediction using denoising autoencoders.
Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design. For the purpose, we propose a novel approach for predicting DTI using a GNN that directly incorporates the 3D structure of a protein-ligand complex. We also apply a distance-aware graph attention algorithm with gate augmentatio…
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-d…
Model predicts stable molecules with AI and physics constraints.
Researchers use active subspaces to quantify uncertainty in deep generative models for molecular design.
Timewarp accelerates molecular dynamics by learning to simulate long timescales.
Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science. Quantum-chemical simulations such as density functional theory (DFT) have been widely used for calculating the molecule properties, however, because of the heavy computational cost, it is difficult to search a…
GAGA accelerates 3D molecular generation by replacing long trajectories with Gaussian approximations.
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate the structure-property relationships directly from chemical data. Here we show that the performance of graph convolutional networks (GCNs) for…
Graph Polish optimizes molecular structures by minimizing changes and maximizing preservation.
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine l…
ASGN uses active semi-supervised learning to predict molecular properties efficiently.
We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings …
XIMP improves molecular property prediction by integrating multiple graph representations.
RC flow learns molecular kinetics in low dimensions.
This paper reviews deep learning and knowledge-based methods for molecular design.
Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery. We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties. Remarkably, we observe that when generated mole…