AIMC Topic: Drug Discovery
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DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.
Artificial intelligence in medicine
Nov 17, 2024
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions predicti...
hERGBoost: A gradient boosting model for quantitative IC prediction of hERG channel blockers.
Computers in biology and medicine
Nov 16, 2024
The human ether-a-go-go-related gene (hERG) potassium channel is pivotal in drug discovery due to its susceptibility to blockage by drug candidate molecules, which can cause severe cardiotoxic effects. Consequently, identifying and excluding potentia...
Deep learning pipeline for accelerating virtual screening in drug discovery.
Scientific reports
Nov 16, 2024
In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce Vir...
Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer.
Methods (San Diego, Calif.)
Nov 15, 2024
Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising approach. Howeve...
Target Fisher: A Consensus Structure-Based Target Prediction Tool, and its Application in the Discovery of Selective MAO-B Inhibitors.
Chemistry (Weinheim an der Bergstrasse, Germany)
Nov 14, 2024
In this work we introduce Target Fisher, a consensus structure-based target prediction tool that integrates molecular docking and machine learning with the aim to aid in the identification of potential biological targets and the optimization of the u...
Strategic partnerships for AI-driven drug discovery: The role of relational dynamics.
Drug discovery today
Nov 14, 2024
Artificial intelligence-driven drug discovery (AIDD) companies hold significant promise for transforming pharmaceutical development, yet little is known about how they manage partnerships with established pharmaceutical firms. To address this researc...
UNIQUE: A Framework for Uncertainty Quantification Benchmarking.
Journal of chemical information and modeling
Nov 14, 2024
Machine learning (ML) models have become key in decision-making for many disciplines, including drug discovery and medicinal chemistry. ML models are generally evaluated prior to their usage in high-stakes decisions, such as compound synthesis or exp...
Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.
International journal of molecular sciences
Nov 14, 2024
The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review explores how recent developments in machine learning (ML) are significantly impacting target-based ...
Improving drug-target interaction prediction through dual-modality fusion with InteractNet.
Journal of bioinformatics and computational biology
Nov 11, 2024
In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end,...
Machine learning-based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs.
Pest management science
Nov 8, 2024
BACKGROUND: Insect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in...
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