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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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
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Related Experiment Video

Updated: Feb 17, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Enabling multi-target drug discovery through latent evolutionary optimization and synthesis-aware prioritization

Viet Thanh Duy Nguyen1, Phuc Pham1, Truong-Son Hy2

  • 1Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, USA.

Communications Chemistry
|February 15, 2026
PubMed
Summary
This summary is machine-generated.

EVOSYNTH, a novel framework, generates multi-target drugs by combining latent evolution and synthesis-aware prioritization. This approach overcomes limitations of single-target therapies and traditional polypharmacy for complex diseases.

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Area of Science:

  • Drug Discovery
  • Computational Chemistry
  • Systems Biology

Background:

  • Complex diseases involve interconnected pathways, rendering single-target therapies insufficient due to redundancy.
  • Polypharmacy offers a solution but faces challenges like drug-drug interactions and toxicity.

Purpose of the Study:

  • Introduce EVOSYNTH, a modular framework for multi-target drug discovery.
  • Enhance the generation and prioritization of drug candidates with high translational potential.

Main Methods:

  • Utilize latent evolution to navigate a latent space for identifying multi-target candidates.
  • Employ synthesis-aware prioritization for evaluating retrosynthetic feasibility and cost-benefit.

Main Results:

  • EVOSYNTH demonstrated superior performance in dual inhibition of JNK3/GSK3β (Alzheimer's) and PI3K/PARP1 (ovarian cancer).
  • Achieved higher predicted affinities, increased scaffold diversity, and reduced synthesis costs compared to baseline models.

Conclusions:

  • EVOSYNTH effectively integrates target-driven generation with practical synthesizability.
  • Establishes a scalable framework for multi-target and polypharmacological drug discovery.