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.
A new method for virtual drug screening detects top treatments.
Understanding the phenotypic drug response on cancer cell lines plays a vital rule in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to test their models and methods. Previously, most research in these ar…
Study improves drug synergy prediction using ensemble learning.
Deep neural network identifies potential SARS-CoV-2 inhibitors.
Deep learning uses ROC cost functions to improve virtual screening accuracy.
ChemCPA predicts cellular responses to novel drugs using transfer learning.
Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recent advances in producing large drug screens against cancer cell lines provided an opportunity to apply machine learning methods for this purpose. In addition to cytotoxi…
Unified model learns from proteins and ligands for drug design.
Machine learning predicts NaV1.7 inhibitors, leading to effective drug K1.
Bayesian methods improve drug discovery experiment design.
Proposes a multi-view architecture for drug-target interaction prediction.
AI and HPC help screen millions of molecules for SARS-CoV-2 treatments.
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…
PDBAL targets experiments for probabilistic models to maximize insights.
Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computat…
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding an…
We propose a novel transfer learning approach for orphan screening called corresponding projections. In orphan screening the learning task is to predict the binding affinities of compounds to an orphan protein, i.e., one for which no training data is available. The identification of compounds with high affinity is a ce…
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is usually done in Phase IV clinical trials, but there are still plenty which remain undiscovered when the drugs are put on the market. Such acc…
Develops a scalable model for drug combination prediction in cancer.
A method selects candidates based on predictions with statistical control.
New method uses Riemannian geometry to describe molecular shapes.
SynthBH uses synthetic data to control FDR in multiple testing.
Network medicine predicts repurposable drugs for COVID-19.
Semi-supervised learning improves QSAR model predictions for novel compounds.
Generates natural product-like compounds using GPT models.
The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However,…
Co-Diffusion predicts drug-target affinity by learning latent manifolds and diffusion, improving generalization.
With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides huge opportunities to improve pharmaceutical research and development. One significant application is the purpose prediction of small molecule compounds, aiming to specify therapeutic propertie…
Model predicts drug overdose hotspots using EMS and toxicology data.
High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry standard B-score method, work on individual compound plates and do not exploit cross…
Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the Opioid Receptor and other members of the GPCR family, traverse many non-crystallographic states. We discover new conformational states of …
DOCKSTRING simplifies docking simulations for better drug design benchmarks.
RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
Paper introduces a method to predict molecule properties from diverse data sources.
In drug-discovery-related tasks such as virtual screening, machine learning is emerging as a promising way to predict molecular properties. Conventionally, molecular fingerprints (numerical representations of molecules) are calculated through rule-based algorithms that map molecules to a sparse discrete space. However,…
DeepSIBA predicts biological effects of chemical structures using graph neural networks.
Bayesian framework for analyzing heterogeneous covariance data with a novel MoE-Wishart model.
Virtual screening (VS) is widely used during computational drug discovery to reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to predict new compound-protein interactions (CPIs) from known CPI network data using several methods, including machine learning and data mining. Although CGBVS facilitat…
Deep filtering improves robustness of models from noisy, sparse data.
New method uses Riemannian geometry to quantify molecular shapes.
Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In this paper, we approach this problem in Bayesian decision framework. We first derive the B…
CSLVAE generates large chemical libraries efficiently.
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…
Deep learning methods such as multitask neural networks have recently been applied to ligand-based virtual screening and other drug discovery applications. Using a set of industrial ADMET datasets, we compare neural networks to standard baseline models and analyze multitask learning effects with both random cross-valid…
New method designs antimicrobial peptides with high potency and low toxicity.
With the increased availability of large databases of electronic health records (EHRs) comes the chance of enhancing health risks screening. Most post-marketing detections of adverse drug reaction (ADR) rely on physicians' spontaneous reports, leading to under reporting. To take up this challenge, we develop a scalable…
In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the -th order convolution operator and the adaptive filtering module. Importantly, our framework of High-order and Adaptive Graph Convo…