Artificial intelligence for drug discovery: Resources, methods, and applications
W Chen, X Liu, S Zhang, S Chen - Molecular Therapy-Nucleic Acids, 2023 - cell.com
Conventional wet laboratory testing, validations, and synthetic procedures are costly and
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …
Utilizing graph machine learning within drug discovery and development
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …
biotechnology industries for its ability to model biomolecular structures, the functional …
Machine learning methods in drug discovery
The advancements of information technology and related processing techniques have
created a fertile base for progress in many scientific fields and industries. In the fields of drug …
created a fertile base for progress in many scientific fields and industries. In the fields of drug …
Machine learning methods, databases and tools for drug combination prediction
L Wu, Y Wen, D Leng, Q Zhang, C Dai… - Briefings in …, 2022 - academic.oup.com
Combination therapy has shown an obvious efficacy on complex diseases and can greatly
reduce the development of drug resistance. However, even with high-throughput screens …
reduce the development of drug resistance. However, even with high-throughput screens …
[HTML][HTML] Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics
With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the
first documented pandemic due to a coronavirus that continues to be a major health …
first documented pandemic due to a coronavirus that continues to be a major health …
Machine learning in pharmacometrics: Opportunities and challenges
M McComb, R Bies… - British Journal of Clinical …, 2022 - Wiley Online Library
The explosive growth in medical devices, imaging and diagnostics, computing, and
communication and information technologies in drug development and healthcare has …
communication and information technologies in drug development and healthcare has …
Machine learning and pharmacometrics for prediction of pharmacokinetic data: differences, similarities and challenges illustrated with rifampicin
Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development
to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic …
to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic …
Artificial intelligence for quantitative modeling in drug discovery and development: An innovation and quality consortium perspective on use cases and best practices
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered
in a new era of possibilities across various scientific domains. One area where these …
in a new era of possibilities across various scientific domains. One area where these …
Potential for chemistry in multidisciplinary, interdisciplinary, and transdisciplinary teaching activities in higher education
JG Hardy, S Sdepanian, AF Stowell… - Journal of Chemical …, 2021 - ACS Publications
For some professionally, vocationally, or technically oriented careers, curricula delivered in
higher education establishments may focus on teaching material related to a single …
higher education establishments may focus on teaching material related to a single …
Artificial intelligence: from buzzword to useful tool in clinical pharmacology
The advent of artificial intelligence (AI) in clinical pharmacology and drug development is
akin to the dawning of a new era. Previously dismissed as merely technological hype, these …
akin to the dawning of a new era. Previously dismissed as merely technological hype, these …