PMF-CPI: assessing drug selectivity with a pretrained multi-functional model for compound–protein interactions
Compound–protein interactions (CPI) play significant roles in drug development. To avoid
side effects, it is also crucial to evaluate drug selectivity when binding to different targets …
side effects, it is also crucial to evaluate drug selectivity when binding to different targets …
UnCorrupt SMILES: a novel approach to de novo design
Generative deep learning models have emerged as a powerful approach for de novo drug
design as they aid researchers in finding new molecules with desired properties. Despite …
design as they aid researchers in finding new molecules with desired properties. Despite …
Chemical library design, QSAR modeling and molecular dynamics simulations of naturally occurring coumarins as dual inhibitors of MAO-B and AChE
Coumarins are a highly privileged scaffold in medicinal chemistry. It is present in many
natural products and is reported to display various pharmacological properties. A large …
natural products and is reported to display various pharmacological properties. A large …
AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology
Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes
other than the physiological ligands is a challenge for in silico screening campaigns. Here …
other than the physiological ligands is a challenge for in silico screening campaigns. Here …
[PDF][PDF] pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures
Motivation G protein-coupled receptors (GPCRs) can selectively bind to many types of
ligands, ranging from light-sensitive compounds, ions, hormones, pheromones and …
ligands, ranging from light-sensitive compounds, ions, hormones, pheromones and …
Exploring natural products as multi-target-directed drugs for Parkinson's disease: an in-silico approach integrating QSAR, pharmacophore modeling, and molecular …
Parkinson's disease is a neurodegenerative disorder characterized by the progressive loss
of dopaminergic neurons in the midbrain. Current treatments provide limited symptomatic …
of dopaminergic neurons in the midbrain. Current treatments provide limited symptomatic …
AI & experimental-based discovery and preclinical IND-enabling studies of selective BMX inhibitors for development of cancer therapeutics
The current work aims to design and provide a preliminary IND-enabling study of selective
BMX inhibitors for cancer therapeutics development. BMX is an emerging target, more …
BMX inhibitors for cancer therapeutics development. BMX is an emerging target, more …
General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach
Abstract Adenosine receptors (A1, A2a, A2b, A3) play critical roles in cellular signaling and
are implicated in various physiological and pathological processes, including inflammations …
are implicated in various physiological and pathological processes, including inflammations …
Machine Learning Approaches to Predict the Selectivity of Compounds against HDAC1 and HDAC6
The design of compounds selectively binding to specific isoforms of histone deacetylases
(HDACs) is ongoing research to prevent adverse side effects. Two of the most studied …
(HDACs) is ongoing research to prevent adverse side effects. Two of the most studied …
Concentration-Dependent bidirectional regulation of adenosine receptor A1 explored through machine learning
Q Yang, L Fan, E Hao, X Hou, J Deng, Z **a… - … and Theoretical Chemistry, 2024 - Elsevier
Objective This study aims to predict the IC50 of adenosine receptor A1 agonists using
machine learning methods, demonstrating the concentration-dependent bidirectional …
machine learning methods, demonstrating the concentration-dependent bidirectional …