AIMC Topic: Drug Design
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Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design.
Briefings in bioinformatics
Jan 17, 2022
Artificial intelligence (AI)-based drug design has great promise to fundamentally change the landscape of the pharmaceutical industry. Even though there are great progress from handcrafted feature-based machine learning models, 3D convolutional neura...
Computational anti-COVID-19 drug design: progress and challenges.
Briefings in bioinformatics
Jan 17, 2022
Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the developmen...
Artificial intelligence in drug discovery: applications and techniques.
Briefings in bioinformatics
Jan 17, 2022
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give a...
Artificial Intelligence in Drug Design: Are We Still There?
Current topics in medicinal chemistry
Jan 1, 2022
BACKGROUND: The artificial intelligence (AI)-assisted design of drug candidates with novel structures and desired properties has received significant attention in the recent past, so related areas of forward prediction that aim to discover chemical m...
Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery.
Current topics in medicinal chemistry
Jan 1, 2022
BACKGROUND: The lengthy and expensive process of developing a novel medicine often takes many years and entails a significant financial burden due to its poor success rate. Furthermore, the processing and analysis of quickly expanding massive data ne...
Artificial Intelligence in Vaccine and Drug Design.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in recent years. There has also been tremendous growth in the fields of data science, informatics, and ar...
Deep Learning in Therapeutic Antibody Development.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
Deep learning applied to antibody development is in its adolescence. Low data volumes and biological platform differences make it challenging to develop supervised models that can predict antibody behavior in actual commercial development steps. But ...
Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their intera...
Deep Learning Applied to Ligand-Based De Novo Drug Design.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on ...
Deep Learning in Structure-Based Drug Design.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques that take into account the structure of the macromolecular target to predict compounds that are likely...
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