AIMC Topic: Drug Discovery

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Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review.

Mini reviews in medicinal chemistry
Drug discovery is a complex and iterative process, making it ideal for using artificial intelligence (AI). This paper uses a bibliometric approach to reveal AI's trend and underlying structure in drug discovery (AIDD). A total of 4310 journal article...

[Omics and cell controlling technology for drug discovery].

Nihon yakurigaku zasshi. Folia pharmacologica Japonica
Knowledge Palette, Inc. is a start-up company that aims to overcome incurable diseases by applying the world's most accurate single-cell level and bulk level transcriptome technology to obtain large-scale data on the state of cells treated with vario...

Artificial Intelligence for the Management of Breast Cancer: An Overview.

Current drug discovery technologies
Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast can...

Accelerating COVID-19 Drug Discovery with High-Performance Computing.

Methods in molecular biology (Clifton, N.J.)
The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has there been such global demand for a therapeutic treatment to be identified as a matter of such urgenc...

Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls.

Methods in molecular biology (Clifton, N.J.)
Building and analyzing knowledge graphs (KGs) to aid drug discovery is a topical area of research. A salient feature of KGs is their ability to combine many heterogeneous data sources in a format that facilitates discovering connections. The utility ...

Knowledge Graphs and Their Applications in Drug Discovery.

Methods in molecular biology (Clifton, N.J.)
Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing o...

Edge, Fog, and Cloud Against Disease: The Potential of High-Performance Cloud Computing for Pharma Drug Discovery.

Methods in molecular biology (Clifton, N.J.)
The high-performance computing (HPC) platform for large-scale drug discovery simulation demands significant investment in speciality hardware, maintenance, resource management, and running costs. The rapid growth in computing hardware has made it pos...

Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.

Current drug discovery technologies
Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its...

AI-Driven Enhancements in Drug Screening and Optimization.

Methods in molecular biology (Clifton, N.J.)
The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, qua...