AIMC Topic: Drug Discovery

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A deep learning model based on the BERT pre-trained model to predict the antiproliferative activity of anti-cancer chemical compounds.

SAR and QSAR in environmental research
Identifying new compounds with minimal side effects to enhance patients' quality of life is the ultimate goal of drug discovery. Due to the expensive and time-consuming nature of experimental investigations and the scarcity of data in traditional QSA...

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.

Journal of chemical information and modeling
Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and researc...

Mining biology for antibiotic discovery.

PLoS biology
The rise of antibiotic resistance calls for innovative solutions. The realization that biology can be mined digitally using artificial intelligence has revealed a new paradigm for antibiotic discovery, offering hope in the fight against superbugs.

"Several birds with one stone": exploring the potential of AI methods for multi-target drug design.

Molecular diversity
Drug discovery is a time-consuming and expensive process. Artificial intelligence (AI) methodologies have been adopted to cut costs and speed up the drug development process, serving as promising in silico approaches to efficiently design novel drug ...

ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training.

Journal of chemical information and modeling
Conformer ranking is a crucial task for drug discovery, with methods for generating conformers often based on molecular (meta)dynamics or sophisticated sampling techniques. These methods are constrained by the underlying force computation regarding r...

CPIScore: A Deep Learning Approach for Rapid Scoring and Interpretation of Protein-Ligand Binding Interactions.

Journal of chemical information and modeling
Protein-ligand binding affinity prediction is a crucial and challenging task in the field of drug discovery. However, traditional simulation-based computational approaches are often prohibitively time-consuming, limiting their practical utility. In t...

vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening.

International journal of molecular sciences
The enthusiastic adoption of make-on-demand chemical libraries for virtual screening has highlighted the need for methods that deliver improved hit-finding discovery rates. Traditional virtual screening methods are often inaccurate, with most compoun...

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.

Artificial intelligence in medicine
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
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
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...