AIMC Topic: Drug Discovery

Clear Filters Showing 21 to 30 of 1689 articles

SHIFT-DRP: Dynamic Multi-Scale Active Learning for Drug Response Prediction.

Journal of chemical information and modeling
Deep learning models show promise for drug response prediction in personalized cancer treatment, but exhibit limited prediction capability for novel drug-cell line combinations due to insufficient coverage of the chemical spaces in training data. The...

Descriptor-First Approach for ADMET Prediction in the PolarisHub Antiviral Challenge.

Journal of chemical information and modeling
The prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties remains a central bottleneck in small-molecule discovery. We present the third-place solution from the PolarisHub Antiviral Competition, covering five ...

Fingerprint-Based Machine Learning for SARS-CoV-2 and MERS-CoV Inhibition: Highlighting the Potential of Bayesian Neural Networks.

Journal of chemical information and modeling
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV) are two important targets in current drug discovery, mainly due to the COVID-19 pandemic and the MERS-CoV outbreaks in recent yea...

BioFusionDTI: Assimilating Graph and Sequence Modalities for Generalizable Drug-Target Interaction Prediction.

Journal of chemical information and modeling
Accurate prediction of drug-target interactions (DTIs) is essential for drug discovery and repurposing. Despite recent advances, deep learning models often exhibit limited generalization under realistic cold-start scenarios and suffer from poor inter...

ProfhEX: Empowering Early Drug Discovery with Machine Learning-Based Target Profiling and Liability Prediction.

Journal of chemical information and modeling
The drug discovery process is inherently lengthy, complex, and costly, with high attrition rates driven by safety concerns, limited efficacy, and regulatory barriers. AI-driven computational methods have become crucial in accelerating this process by...

COMET: A Machine-Learning Framework Integrating Ligand-Based and Target-Based Algorithms for Elucidating Drug Targets.

Journal of medicinal chemistry
Elucidation of the potential molecular targets of a bioactive compound, a process known as target-fishing, is a critical task in drug discovery. Computational methods can efficiently narrow down the candidate targets for subsequent experimental valid...

Artificial Intelligence Tools for Drug Target Discovery Research: Database, Tools, Applications, and Challenges.

Chemistry (Weinheim an der Bergstrasse, Germany)
The identification of drug targets remains one of the most critical challenges in pharmaceutical research. The rapid progress of artificial intelligence (AI) is significantly advancing this landscape by enabling more efficient and accurate drug-targe...

Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning.

Nature communications
Developing selective kinase inhibitors is challenging due to the conserved kinase structures and costly kinome profiling experiments, highlighting the need for accurate prediction of kinase-inhibitor affinity and specificity. Here we present MMCLKin,...

DeepTargetClass: a web-based platform for predicting protein target classes of small molecules.

Journal of computer-aided molecular design
The identification of protein target classes is a key step in drug discovery, as it enables prioritization of screening campaigns and supports target-based drug repurpose. In this study, we developed a deep-learning pipeline based on a multilayer per...

AI-driven discovery of antiretroviral drug bictegravir and etravirine as inhibitors against monkeypox and related poxviruses.

Communications biology
Monkeypox virus (MPXV) caused the 2022-2023 global mpox and the concurrent outbreaks in Africa, disproportionately affecting immunocompromised individuals such as people living with HIV. With no approved treatment available, we developed a robust art...