Machine learning in preclinical drug discovery
Drug-discovery and drug-development endeavors are laborious, costly and time consuming.
These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of …
These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of …
Revolutionizing medicinal chemistry: the application of artificial intelligence (AI) in early drug discovery
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical
industry and research, where it has been utilized to efficiently identify new chemical entities …
industry and research, where it has been utilized to efficiently identify new chemical entities …
On some novel similarity-based functions used in the ML-based q-RASAR approach for efficient quantitative predictions of selected toxicity end points
The novel quantitative read-across structure–activity relationship (q-RASAR) approach uses
read-across-derived similarity functions in the quantitative structure–activity relationship …
read-across-derived similarity functions in the quantitative structure–activity relationship …
A review on the recent applications of deep learning in predictive drug toxicological studies
Drug toxicity prediction is an important step in ensuring patient safety during drug design
studies. While traditional preclinical studies have historically relied on animal models to …
studies. While traditional preclinical studies have historically relied on animal models to …
Data-driven toxicity prediction in drug discovery: Current status and future directions
N Wang, X Li, J ** and prioritization of a very large number of diverse chemical …
Leveraging cell painting images to expand the applicability domain and actively improve deep learning quantitative structure–activity relationship models
The search for chemical hit material is a lengthy and increasingly expensive drug discovery
process. To improve it, ligand-based quantitative structure–activity relationship models have …
process. To improve it, ligand-based quantitative structure–activity relationship models have …
Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning
G Wang, H Feng, M Du, Y Feng… - Journal of Chemical …, 2024 - ACS Publications
Toxicity is paramount for comprehending compound properties, particularly in the early
stages of drug design. Due to the diversity and complexity of toxic effects, it became a …
stages of drug design. Due to the diversity and complexity of toxic effects, it became a …
Dual-payload Antibody–drug Conjugates: Taking a Dual Shot
J Tao, Y Gu, W Zhou, Y Wang - European Journal of Medicinal Chemistry, 2024 - Elsevier
Antibody-drug conjugates (ADCs) enable the precise delivery of cytotoxic agents by
conjugating small-molecule drugs with monoclonal antibodies (mAbs). Over recent decades …
conjugating small-molecule drugs with monoclonal antibodies (mAbs). Over recent decades …