In silico methods and tools for drug discovery

B Shaker, S Ahmad, J Lee, C Jung, D Na - Computers in biology and …, 2021 - Elsevier
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 …

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

R Gupta, D Srivastava, M Sahu, S Tiwari, RK Ambasta… - Molecular …, 2021 - Springer
Drug designing and development is an important area of research for pharmaceutical
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

G **ong, Z Wu, J Yi, L Fu, Z Yang, C Hsieh… - Nucleic acids …, 2021 - academic.oup.com
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 …

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

Y Myung, AGC de Sá, DB Ascher - Nucleic acids research, 2024 - academic.oup.com
Evaluating pharmacokinetic properties of small molecules is considered a key feature in
most drug development and high-throughput screening processes. Generally …

[HTML][HTML] Molecular modeling in drug discovery

TI Adelusi, AQK Oyedele, ID Boyenle… - Informatics in Medicine …, 2022 - Elsevier
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 …

A knowledge-guided pre-training framework for improving molecular representation learning

H Li, R Zhang, Y Min, D Ma, D Zhao, J Zeng - Nature Communications, 2023 - nature.com
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 …

Machine learning for synergistic network pharmacology: a comprehensive overview

F Noor, M Asif, UA Ashfaq, M Qasim… - Briefings in …, 2023 - academic.oup.com
Network pharmacology is an emerging area of systematic drug research that attempts to
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 …

ADMET modeling approaches in drug discovery

LLG Ferreira, AD Andricopulo - Drug discovery today, 2019 - Elsevier
Highlights•ADMET modeling plays a pivotal part in drug discovery.•Chemoinformatics has
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 …