Prescriptions

Atom-level generative foundation model for molecular interaction with pockets

Understanding molecular interactions is essential to structural biology and drug discovery. Despite the progress of AI models in revealing and exploiting the interaction mechanisms for various applications, they are predominantly tailored to specific tasks without fully exploiting the underlying transferability across molecular data and tasks. Here, we present PocketXMol, an atom-level generative ...

Improving Protein Interaction Prediction in GenPPi: A Novel Interaction Sampling Approach Preserving Network Topology

Computational prediction of protein-protein interactions (PPIs) is crucial for understanding cell biology and drug development, offering an alternative to costly experimental methods. The original GenPPi software advanced ab initio PPI network prediction from bacterial genomes but was limited by its reliance on high sequence similarity. This work introduces GenPPi 1.5 to enhance these predictive c...

Predicting antifolate resistance in the unculturable fungal pathogen Pneumocystis jirovecii

Pneumocystis jirovecii is a fungal pathogen causing Pneumocystis pneumonia in humans, mainly in immunocompromised individuals. Infections by P. jirove...

Automated quantification of ecological interactions from video

Ecological interactions, such as predation, are fundamental events that underlie the flow and distribution of energy through food webs. Yet, directly ...

The genetic architecture of the human bZIP interaction network

Generative biology holds the promise to transform our ability to design and understand living systems by creating novel proteins, pathways, and organi...

Pocket-based molecule generation with an SE(3)-equivariant language model leads to a potent and selective HPK1 inhibitor with in vivo efficacy

Deep learning shows promise in structure-based drug discovery, yet challenges persist in generating pharmacologically plausible molecules with valid 3...

In Silico Design of APOE ɛ4 Interaction Inhibitor Peptides for Alzheimer’s Disease

Protein-protein interactions (PPIs) are essential for cellular functions, and their aberrant formation contributes to neurodegenerative diseases. Alzh...

SAM-DTI: A Spatial Attention Model for Drug-Target Interaction Prediction

Drug-target protein interaction (DTI) prediction is an important area of research in drug repurposing and discovery. Laboratory experiments for explor...

Prediction of TdP Arrhythmia Risk Through Molecular Simulations of Conformation-specific Drug Interactions with the hERG K+, NaV1.5, and CaV1.2 Channels

Unintended block of cardiac ion channels, particularly hERG (KV11.1), remains a key concern in drug development as disruption of ion channel function ...

PathPCNet: Pathway Principal Component-Based Interpretable Framework for Drug Sensitivity Prediction

Precision medicine aims to identify significant biomarkers and effective drugs tailored to individual genomic profiles, thereby enabling personalized ...

Disease-specific tau polymorphs define unique protein interaction networks across proteinopathies

Tau protein aggregates exhibit distinct conformations across tauopathies, but their disease-specific protein interactions remain poorly understood. He...

Predicting Clinical Outcomes in Helicobacter pylori-positive Patients using Supervised Learning through the Integration of Demographic and Genomic Features

Helicobacter pylori (H. pylori) infection is widespread globally and is linked to outcomes ranging from chronic gastritis to gastric cancer. However, ...

Variational autoencoder for interpretable seizure onset phases detection

In this study, we describe a deep learning framework for automated seizure annotation in stereo electroencephalography (SEEG) data of patients with fo...

Breaking the culture habit: metagenomic diagnosis of companion animal skin infections

Skin infections have been described as the primary cause for presentation in veterinary small animal practices and they frequently result in prescript...

TransStop, a genomic language model for the pan-drug prediction of translational readthrough efficacy

Premature termination codons (PTCs) are a major cause of genetic diseases, but the efficacy of therapeutic readthrough agents is highly context-depend...

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of ac...

FUSED: Cross-Domain Integration of Foundation Models for Cancer Drug Response Prediction

AI-driven methods for predicting drug responses hold promise for advancing personalized cancer therapy, but cancer heterogeneity and the high cost of ...

Machine learning driven acceleration of biopharmaceutical formulation development using Excipient Prediction Software (ExPreSo)

Formulation development of protein biopharmaceuticals has become increasingly challenging due to new modalities and higher target drug substance conce...

The Multiple Approaches for Drug-Drug Interaction Extraction using Machine learning and transformer based Model

This research paper investigates a machine learning based approach for Drug-Drug Interaction (DDI) extraction for determining the side effects of mult...

Fragment-Guided New Therapeutic Molecule Discovery and Mapping of Clinically Relevant Interactomes

Therapeutic intervention solutions for complex diseases depend on the targeted modulation of key pathways in pathology. While growing clinical needs c...