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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Best practices for artificial intelligence in life sciences research.

Vladimir A Makarov¹, Terry Stouch², Brandon Allgood³

¹Pistoia Alliance, 401 Edgewater Place, Suite 600, Wakefield, MA 01880, USA.

Drug Discovery Today

|January 25, 2021

Summary

This summary is machine-generated.

This study outlines 11 best practices for leveraging artificial intelligence (AI) and machine learning (ML) in pharmaceutical and biotechnology research. These guidelines cover data management, technology implementation, and organizational strategies for successful AI/ML adoption.

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Area of Science:

Pharmaceutical research
Biotechnology
Computational science

Background:

Artificial intelligence (AI) and machine learning (ML) offer transformative potential in drug discovery and development.
Successful implementation of AI/ML requires strategic planning across multiple organizational levels.

Purpose of the Study:

To define 11 essential best practices for the effective application of AI and ML in pharmaceutical and biotechnology research.
To provide a framework for optimizing AI/ML integration from data to organizational management.

Main Methods:

Literature review and expert consensus on AI/ML implementation in R&D.
Categorization of best practices across data, technology, and organizational management domains.

Main Results:

Eleven key best practices were identified for successful AI and ML deployment.
These practices address critical aspects of data quality, technological infrastructure, and change management.

Conclusions:

Adherence to these best practices can significantly enhance the success rate of AI/ML initiatives in the pharmaceutical and biotechnology sectors.
A holistic approach integrating data, technology, and organizational readiness is crucial for realizing the full potential of AI/ML.