AIMC Topic: Ligands

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Prediction of Activity and Selectivity Profiles of Sigma Receptor Ligands Using Machine Learning Approaches.

Journal of chemical information and modeling
Sigma (σ) receptors (SRs) have emerged as important therapeutic targets due to their roles in various biological pathways. They are classified into two subtypes: S1R, primarily distributed in the central nervous system and related to neuroprotection ...

Computationally Designed Nanobinders as Affinity Ligands in Diagnostic and Therapeutic Applications.

Journal of the American Chemical Society
Detecting protein biomarkers is critical in fundamental research and clinical investigations of extracellular vesicles (EVs). Despite the prevalent use of antibodies as recognition elements, their application is often limited by challenges such as cr...

AI-Designed Molecules in Drug Discovery, Structural Novelty Evaluation, and Implications.

Journal of chemical information and modeling
Achieving structural novelty in drug discovery remains a critical challenge. Artificial intelligence (AI) has demonstrated remarkable potential in deciphering the complex relationships between molecular structures and activities from vast amounts of ...

GATRsite: RNA-Ligand Binding Site Prediction Using Graph Attention Networks and Pretrained RNA Language Models.

Journal of chemical information and modeling
Identifying functional sites of RNA, particularly those where small molecules bind, is crucial for understanding related biological processes and advancing drug design. Small molecule therapies, compared to traditional protein-targeted therapies, hav...

Ligand supplementation restores the cancer therapy efficacy of the antirheumatic drug auranofin from serum inactivation.

Nature communications
Auranofin, an FDA-approved antirheumatic gold drug, has gained ongoing interest in clinical studies for treating advanced or recurrent tumors. However, gold ion's dynamic thiol exchange nature strongly attenuates its bioactivity due to the fast forma...

Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.

Journal of chemical theory and computation
Binding free energies are key elements in understanding and predicting the strength of protein-drug interactions. While classical free energy simulations yield good results for many purely organic ligands, drugs, including transition metal atoms, oft...

Sequence-based virtual screening using transformers.

Nature communications
Protein-ligand interactions play central roles in myriad biological processes and are of key importance in drug design. Deep learning approaches are becoming cost-effective alternatives to high-throughput experimental methods for ligand identificatio...

Augmenting MACCS Keys with Persistent Homology Fingerprints for Protein-Ligand Binding Classification.

Journal of chemical information and modeling
Machine learning has become an essential tool in computational drug design, enabling models to uncover patterns in molecular data and predict protein-ligand interactions. This study introduces a novel approach by integrating persistence images with M...

Benchmarking 3D Structure-Based Molecule Generators.

Journal of chemical information and modeling
To understand the benefits and drawbacks of 3D combinatorial and deep learning generators, a novel benchmark was created focusing on the recreation of important protein-ligand interactions and 3D ligand conformations. Using the BindingMOAD data set w...

AF3Score: A Score-Only Adaptation of AlphaFold3 for Biomolecular Structure Evaluation.

Journal of chemical information and modeling
Scoring biomolecular complexes remains central to structural modeling efforts. Recent studies suggest that AlphaFold (AF) - a revolutionary deep learning model for biomolecular structure prediction - has implicitly learned an approximate biophysical ...