AIMC Topic: Drug Discovery

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SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Journal of chemical information and modeling
Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into...

Accelerating drug discovery, development, and clinical trials by artificial intelligence.

Med (New York, N.Y.)
Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation in drug development. This paper aims to deliver a comprehensive analysis of the progress in AI-assis...

Deep learning large-scale drug discovery and repurposing.

Nature computational science
Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochon...

Perspectives on the use of machine learning for ADME prediction at AstraZeneca.

Xenobiotica; the fate of foreign compounds in biological systems
A drug's pharmacokinetic (PK) profile will determine its dose and the frequency of administration as well as the likelihood of observing any adverse drug reactions.It is important to understand these PK properties as early as possible in the drug dis...

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.

Journal of computer-aided molecular design
The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success a...

MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.

European journal of medicinal chemistry
Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in...

FitScore: a fast machine learning-based score for 3D virtual screening enrichment.

Journal of computer-aided molecular design
Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large d...

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.

AAPS PharmSciTech
Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations....

Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation.

Future medicinal chemistry
Build a virtual screening model for ULK1 inhibitors based on artificial intelligence. Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million co...

Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.

International journal of molecular sciences
Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has s...