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In silico methods and tools for drug discovery
In the past, conventional drug discovery strategies have been successfully employed to
develop new drugs, but the process from lead identification to clinical trials takes more than …
develop new drugs, but the process from lead identification to clinical trials takes more than …
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
Because undesirable pharmacokinetics and toxicity of candidate compounds are the main
reasons for the failure of drug development, it has been widely recognized that absorption …
reasons for the failure of drug development, it has been widely recognized that absorption …
Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction
Evaluating pharmacokinetic properties of small molecules is considered a key feature in
most drug development and high-throughput screening processes. Generally …
most drug development and high-throughput screening processes. Generally …
[HTML][HTML] Molecular modeling in drug discovery
With the financial requirements and high time associated with bringing a commercial drug to
the market, the application of computer-aided drug design has been recognized as a …
the market, the application of computer-aided drug design has been recognized as a …
A knowledge-guided pre-training framework for improving molecular representation learning
Learning effective molecular feature representation to facilitate molecular property prediction
is of great significance for drug discovery. Recently, there has been a surge of interest in pre …
is of great significance for drug discovery. Recently, there has been a surge of interest in pre …
Machine learning for synergistic network pharmacology: a comprehensive overview
Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …
understand drug actions and interactions with multiple targets. Network pharmacology has …
Evaluation of free online ADMET tools for academic or small biotech environments
J Dulsat, B López-Nieto, R Estrada-Tejedor, JI Borrell - Molecules, 2023 - mdpi.com
For a new molecular entity (NME) to become a drug, it is not only essential to have the right
biological activity also be safe and efficient, but it is also required to have a favorable …
biological activity also be safe and efficient, but it is also required to have a favorable …
ADMET modeling approaches in drug discovery
Highlights•ADMET modeling plays a pivotal part in drug discovery.•Chemoinformatics has
evolved into robust machine learning approaches.•Comprehensive web-based platforms for …
evolved into robust machine learning approaches.•Comprehensive web-based platforms for …
A drug-likeness toolbox facilitates ADMET study in drug discovery
CY Jia, JY Li, GF Hao, GF Yang - Drug discovery today, 2020 - Elsevier
Highlights•Online resources facilitate in silico drug-likeness study.•Databases gathering
high quality and up to date data are essential for drug-likeness evaluation.•Web servers for …
high quality and up to date data are essential for drug-likeness evaluation.•Web servers for …