Antibodies play critical roles in modern medicine, serving as diagnostics and therapeutics for various diseases due to their ability to specifically bind to target antigens. Traditional antibody discovery and optimization methods are time-consuming a...
The rapid evolution of artificial intelligence (AI) is redefining biomedicine, placing itself at the forefront of groundbreaking discoveries in molecular biology, genomics, drug discovery, diagnostics, and beyond [...].
European journal of medicinal chemistry
Dec 13, 2024
Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identific...
The rapid advancement of generative artificial intelligence (AI) is reshaping pharmaceutical research and development (R&D), offering opportunities across drug discovery and development. Generative AI (GenAI) enhances productivity by enabling virtual...
Human dose prediction (HDP) is a useful tool for compound optimization in preclinical drug discovery. We describe here our exclusively in silico HDP strategy to triage compound designs for synthesis and experimental profiling. Our goal is a model tha...
Binding affinity prediction has been considered as a fundamental task in drug discovery. Despite much effort to improve accuracy of binding affinity prediction, the prior work considered only macro-level features that can represent the characteristic...
Journal of chemical information and modeling
Dec 11, 2024
Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. assays support high-throughput screening, allowing for efficient detection of toxic substances while conside...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Generating high-quality and drug-like molecules from scratch within the expansive chemical space presents a significant challenge in the field of drug discovery. In prior research, value-based reinforcement learning algorithms have been employed to g...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Drug Target Interaction (DTI) prediction plays a crucial role in in-silico drug discovery, especially for deep learning (DL) models. Along this line, existing methods usually first extract features from drugs and target proteins, and use drug-target ...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this stu...
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