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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Deep learning pipeline for accelerating virtual screening in drug discovery.

Fatima Noor^1,2, Muhammad Junaid³, Atiah H Almalki^4,5

¹Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, 35000, Pakistan.

Scientific Reports

|November 16, 2024

Summary

VirtuDockDL is a new AI platform that speeds up drug discovery by predicting compound effectiveness. It accurately identified inhibitors for the Marburg virus and outperformed other tools in benchmarking tests.

Keywords:

Deep learning (DL)Drug discovery Graph neural network (GNN)Pipeline Python VirtuDockDL

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High Content Screening in Neurodegenerative Diseases

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High Content Screening in Neurodegenerative Diseases

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Published on: January 6, 2012

Area of Science:

Computational chemistry
Artificial intelligence in drug discovery
Bioinformatics

Background:

Traditional drug discovery is expensive and slow.
Need for efficient methods to identify new therapeutic agents.
Viruses like Marburg pose significant global health threats.

Purpose of the Study:

Introduce VirtuDockDL, a deep learning-based platform for efficient drug discovery.
Utilize Graph Neural Networks for predicting compound efficacy.
Validate and benchmark VirtuDockDL's performance against existing tools.

Main Methods:

Developed a Python-based web platform, VirtuDockDL.
Employed Graph Neural Networks for compound analysis.
Validated against Marburg virus VP35 protein and benchmarked on HER2 dataset.

Main Results:

Identified non-covalent inhibitors for Marburg virus VP35 protein.
Achieved 99% accuracy, 0.992 F1 score, and 0.99 AUC on the HER2 dataset.
Outperformed DeepChem, AutoDock Vina, RosettaVS, MzDOCK, and PyRMD in predictive accuracy and automation.

Conclusions:

VirtuDockDL offers a rapid, cost-effective solution for drug discovery.
Demonstrates high-affinity inhibitor identification capabilities for various targets (HER2, TEM-1, CYP51).
AI integration in drug discovery promises faster responses to health challenges.