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Related Concept Videos

Drug Discovery: Overview01:26

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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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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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Updated: Jan 13, 2026

Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
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An Evidence-Grounded Research Assistant for Functional Genomics and Drug Target Assessment.

Ksenia Sokolova1,2, Dmitri Kosenkov1,3, Keerthana Nallamotu4,3

  • 1Princeton Precision Health, Princeton University, Princeton, NJ, USA.

Biorxiv : the Preprint Server for Biology
|January 8, 2026
PubMed
Summary
This summary is machine-generated.

Alvessa, an AI research assistant, enhances biological data analysis by grounding answers in evidence and verifying claims. This improves accuracy and traceability in biomedical research.

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Biology
  • Computational Biology

Background:

  • Biological data resources are expanding, but their effective use is hindered by fragmentation and the need for domain expertise.
  • Current large language models in biomedicine often produce unsupported or incorrect claims, lacking provenance.
  • Synthesizing reliable conclusions from diverse biological data is labor-intensive and challenging.

Purpose of the Study:

  • To introduce Alvessa, an evidence-grounded agentic research assistant designed for verifiability in biological research.
  • To address limitations of general-purpose language models in handling complex biomedical data and ensuring claim accuracy.
  • To support reproducible and verifiable AI-assisted biological research.

Main Methods:

  • Alvessa integrates entity recognition, pre-validated biological tools, and data-constrained answer generation.
  • It performs statement-level verification against retrieved records, flagging unsupported claims.
  • Evaluation was conducted on dbQA (LAB-Bench) and GenomeArena benchmarks covering diverse biological questions.

Main Results:

  • Alvessa significantly improves accuracy compared to general-purpose language models on biomedical QA tasks.
  • It performs comparably to coding-centric agents while ensuring fully traceable outputs.
  • Detection of fabricated statements critically depends on access to retrieved evidence, as shown by adversarial testing.

Conclusions:

  • Alvessa offers a verifiable and accurate approach to AI-assisted biological research, overcoming key limitations of existing LLMs.
  • The system demonstrates practical application in drug discovery, identifying candidate targets missed by literature-centered methods.
  • Alvessa and GenomeArena are released to foster reproducible and reliable AI-driven scientific discovery.