We analyze the methods of machine learning and their applications for the design of chemical compounds with antiviral properties. A comprehensive analysis of the molecular mechanisms of viral progression in human cells is one of the promising ways to discover novel prospective targets for the ...
The reviewed techniques include reinforcement learning (RL), transfer learning, and multitask learning. In their well-received review centred on ML for drug discovery, Lo et al. remarked that techniques with increased visibility, as well methods for preventing overfitting, warrant further development ...
machine learninginformaticsdrug safetyPreclinical and clinical safety is one of the major reasons for attrition in the drug discovery and development process. ... C Hasselgren,D Muthas,E Ahlberg,... - John Wiley & Sons, Inc 被引量: 2发表: 2013年 On the use of machine learning methods for...
machine learning and artificial intelligence methods in drug discovery, and indicate a promising future for these technologies; these results should enable researchers, students, and pharmaceutical industry to dive deeper into machine learning and artificial intelligence in a drug discovery and development ...
Recent patents relating to machine learning in drug discovery and screening. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 24,99 € / 30 days ...
Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all sta...
Lavecchia, Antonio. “Machine-learning approaches in drug discovery: methods and applications.” Drug discovery today 20.3 (2015): 318-331. (http://www.sciencedirect.com/science/article/pii/S1359644614004176) Web Search and Recommendation Engines: ...
Methods: We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery. Results: Our experiments demonstrated that there are different patterns in machine lea...
Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all sta...
the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specif i cally, we highlight an array of ML-based ef f orts, across diverse disease areas, to accelerate initial hit discovery, mech...