AIMC Topic: Drug Discovery

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Applications and prospects of cryo-EM in drug discovery.

Military Medical Research
Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to...

BCM-DTI: A fragment-oriented method for drug-target interaction prediction using deep learning.

Computational biology and chemistry
The identification of drug-target interaction (DTI) is significant in drug discovery and development, which is usually of high cost in time and money due to large amount of molecule and protein space. The application of deep learning in predicting DT...

An approach combining deep learning and molecule docking for drug discovery of cathepsin L.

Expert opinion on drug discovery
OBJECTIVES: Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible...

Structure-based drug design with geometric deep learning.

Current opinion in structural biology
Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has be...

Strategy toward Kinase-Selective Drug Discovery.

Journal of chemical theory and computation
Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity has been a major cause for clinical trial failures. The pockets are similar but not identical. Here...

Machine learning approaches to predict drug efficacy and toxicity in oncology.

Cell reports methods
In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical pro...

Federated learning for molecular discovery.

Current opinion in structural biology
Federated Learning enables machine learning across multiple sources of data and alleviates the risk of leaking private information between partners thereby encouraging knowledge sharing and collaborative modelling. Hence, Federated Learning opens the...

Artificial intelligence for compound pharmacokinetics prediction.

Current opinion in structural biology
Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learnin...

DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.

BMC bioinformatics
BACKGROUND: Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the appr...

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery.

Advanced biology
Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead ...