AIMC Topic: Drug Discovery

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Learning Binding Affinities via Fine-Tuning of Protein and Ligand Language Models.

Journal of chemical information and modeling
Accurate in silico prediction of protein-ligand binding affinity is essential for efficient hit identification in large molecular libraries. Commonly used structure-based methods such as docking often fail to rank compounds effectively, and free ener...

Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.

RNA biology
Cancer diagnosis at an early stage is crucial for improving overall health outcomes. However, existing cancer diagnostic techniques are mostly invasive and tend to identify the disease only in its advanced stages. MicroRNAs (miRNAs), which are small ...

iCAM-Net: Interpretable herb-disease association prediction via cross-channel attention and molecular interaction signals.

Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND: Target identification is fundamental to drug discovery, facilitating mechanistic elucidation and therapeutic target identification. Traditional herbal medicines exert therapeutic effects through multi-component, multi-target synergistic m...

μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification.

Journal of computer-aided molecular design
Understanding active functional class (agonist vs antagonist) at the human μ-opioid receptor (μOR) is critical for drug discovery and safety assessment. While recent machine learning models such as ExtraTrees (ET) and message-passing neural networks ...

Identification of hub necroptosis-related targets and discovery of potential natural inhibitors in ulcerative colitis based on bioinformatics and computer-aided drug design.

Journal of computer-aided molecular design
Ulcerative colitis (UC) is a chronic inflammatory bowel disease with a complex pathogenesis and limited treatment options. Recently, necroptosis has been found to play a significant role in UC. This study aimed to investigate necroptosis-related mech...

PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.

Scientific reports
Adverse drug events represent a key challenge in public health, especially concerning drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to...

Unveiling molecular moieties through hierarchical Grad-CAM graph explainability.

BMC bioinformatics
BACKGROUND: Virtual Screening (VS) has become an essential tool in drug discovery, enabling the rapid and cost-effective identification of potential bioactive molecules. Among recent advancements, Graph Neural Networks (GNNs) have gained prominence f...

Fast and Reliable NMR-Based Fragment Scoring for Drug Discovery.

Journal of the American Chemical Society
Fragment-Based Drug Discovery (FBDD) is a powerful strategy used in the development of new therapeutics. Molecular fragments are screened against a target protein, where interactions are typically characterized by a low affinity. Nuclear Magnetic Res...

Metabolite Identification Data in Drug Discovery, Part 2: Site-of-Metabolism Annotation, Analysis, and Exploration for Machine Learning.

Molecular pharmaceutics
The ability to pinpoint and predict sites of metabolism (SoMs) is essential for designing and optimizing effective and safe bioactive small molecules. However, the number of molecules with annotated SoMs is limited, hindering the advancement of data-...

Machine Learning Guided by Physicochemical Principles Enables Generalized Prediction of Small-Molecule Subcellular Localization and Discovery of Targeted Molecules.

Analytical chemistry
Precise subcellular localization is crucial for the design of molecular probes and targeted therapeutics, yet selectively distinguishing organelles with similar physicochemical properties, such as lipid droplets, mitochondria, and the cell membrane, ...