Annual review of pharmacology and toxicology
Dec 12, 2014
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the s...
During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and ...
Schistosomiasis, caused by Schistosoma mansoni, remains a significant public health burden, particularly in endemic regions with limited access to effective treatment. The emergence of resistance to praziquantel necessitates the urgent discovery of n...
The intersection of biosciences and technology has yielded transformative advancements, and Generative Artificial Intelligence (GenAI) started to stand at the forefront of this synergy. In the field of life sciences, GenAI is emerging as a catalyst, ...
Journal of chemical information and modeling
Sep 8, 2025
The transient receptor potential vanilloid 1 (TRPV1) ion channel is a key mediator of pain and inflammation, making it a crucial target for developing new analgesics. Despite progress in understanding TRPV1's role, novel modulators that effectively i...
Journal of chemical information and modeling
Sep 8, 2025
Predicting compound-protein interaction (CPI) plays a critical role in drug discovery and development, but traditional screening experiments consume much time and resources. Therefore, deep learning methods for CPI prediction are popular now. However...
Journal of chemical information and modeling
Sep 8, 2025
Accurate prediction of molecular properties is essential in drug discovery and related fields. However, existing graph neural networks (GNNs) often struggle to simultaneously capture both local and global molecular structures. In this work, we propos...
Journal of chemical information and modeling
Sep 8, 2025
Antimicrobial peptides (AMPs) have emerged as vital candidates in the fight against antibiotic resistance. The traditional processes for AMP design and discovery are often time-consuming and inefficient. Here, we propose the AMPGP model, which employ...
Journal of chemical information and modeling
Sep 8, 2025
Achieving structural novelty in drug discovery remains a critical challenge. Artificial intelligence (AI) has demonstrated remarkable potential in deciphering the complex relationships between molecular structures and activities from vast amounts of ...
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