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.
Deep neural network identifies potential SARS-CoV-2 inhibitors.
Deep learning uses ROC cost functions to improve virtual screening accuracy.
Unified model learns from proteins and ligands for drug design.
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…
New method uses Riemannian geometry to describe molecular shapes.
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…
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 …
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…
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…
Study improves drug synergy prediction using ensemble learning.
Efficiently allocate budgets for LLM-assisted virtual screening to reduce costs.
RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.
Co-Diffusion predicts drug-target affinity by learning latent manifolds and diffusion, improving generalization.
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,…
CSLVAE generates large chemical libraries efficiently.
KANEL combines models for early hit enrichment in virtual screening.
Study improves reliability of neural models for virtual screening.
ChemCPA predicts cellular responses to novel drugs using transfer learning.
DOCKSTRING simplifies docking simulations for better drug design benchmarks.
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. In this report, we decompose the ROCS color force field into "color com…
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…
We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately. These fingerprints are further transformed non-linearly, their inner-product …
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…
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 uses Riemannian geometry to quantify molecular shapes.
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.
Bayesian learning improves reliability of molecular predictions for hit compound discovery.
AI and HPC help screen millions of molecules for SARS-CoV-2 treatments.
PDBAL targets experiments for probabilistic models to maximize insights.
Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and validated. We reanalyze the data generated by a recently pub…
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…
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…
Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques can make use of the increasing amounts of structural data that are beco…
Develops a scalable model for drug combination prediction in cancer.
A method selects candidates based on predictions with statistical control.
SynthBH uses synthetic data to control FDR in multiple testing.
FlowMO uses Gaussian Processes for molecular property prediction with uncertainty.
Network medicine predicts repurposable drugs for COVID-19.
Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering. An important task is to predict the properties of molecules, which serves as the main subroutine in many downstream applications such as virtual screening and drug design. Despite th…
Semi-supervised learning improves QSAR model predictions for novel compounds.
Generates natural product-like compounds using GPT models.
Conformal prediction fails to cover minority classes in imbalanced datasets, but a class-conditional fix improves coverage.
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques t…
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,…