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Drug Discovery: Overview

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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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Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

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Graph neural networks driven acceleration in drug discovery.

Rui Wang¹, Chunlin Zhuang¹

¹The Center for Basic Research and Innovation of Medicine and Pharmacy (MOE), School of Pharmacy, Second Military Medical University, Shanghai 200433, China.

Acta Pharmaceutica Sinica. B

|January 1, 2026

Summary

Graph neural networks (GNNs) are transforming drug discovery by modeling molecules and predicting properties. This accelerates the identification and design of new therapeutics, reducing costs and failures.

Keywords:

De novo drug design Drug–target interaction Graph neural networks Lead discovery Lead optimization Property prediction Synthetic route Virtual screening

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Area of Science:

* Computational chemistry and cheminformatics.
* Artificial intelligence and machine learning.
* Pharmaceutical sciences and drug development.

Background:

* Graph neural networks (GNNs) have rapidly advanced drug design over the last five years.
* GNNs excel at modeling complex molecular structures and their interactions with biological targets.
* Previous methods faced limitations in predictive accuracy and efficiency.

Purpose of the Study:

* To review the interdisciplinary integration of GNNs across the entire drug discovery pipeline.
* To highlight GNN applications from lead discovery to synthetic route design.
* To critically assess the challenges of implementing GNNs in translational medicine.

Main Methods:

* Review of recent literature on GNN applications in drug discovery.
* Analysis of GNNs' impact on molecular property prediction, virtual screening, and de novo design.
* Examination of GNNs in drug repurposing, toxicity assessment, and drug-target interaction prediction.

Main Results:

* GNNs significantly improve predictive accuracy for molecular properties and interactions.
* Generative GNNs accelerate virtual screening and novel molecule design.
* GNN integration reduces drug development costs and minimizes late-stage failures.

Conclusions:

* GNNs are pivotal in modernizing and accelerating drug discovery processes.
* The integration of GNNs enhances efficiency and success rates in therapeutic development.
* Overcoming translational challenges is key to fully realizing GNN potential in medicine.