LLMs for Drug Discovery: A Comprehensive Literature Review

Overview

I’ve completed a comprehensive literature review examining the transformative role of Large Language Models (LLMs) in drug discovery and pharmaceutical development. This review synthesizes current research, identifies key methodologies, and explores future opportunities at the intersection of artificial intelligence and pharmaceutical science.

Key Focus Areas

The literature review covers:

Molecular Property Prediction: How LLMs are revolutionizing the prediction of molecular properties critical for drug development
Target Identification: Applications of language models in identifying and validating drug targets
Lead Optimization: AI-driven approaches to optimizing compound structures for therapeutic efficacy
Clinical Trial Enhancement: Integration of LLMs in clinical research and trial design
Regulatory Considerations: Current challenges and opportunities in AI-assisted drug approval processes

Methodology

This review employed a systematic approach to literature analysis, examining peer-reviewed publications, industry reports, and emerging research from leading pharmaceutical and AI research institutions. The analysis spans both theoretical frameworks and practical implementations across major pharmaceutical companies and research organizations.

Technical Writing Sample

This literature review serves as an example of my technical writing capabilities, demonstrating:

Research Synthesis: Ability to analyze and integrate complex scientific information across multiple domains
Technical Communication: Clear presentation of sophisticated AI/ML concepts for diverse audiences
Critical Analysis: Evaluation of current methodologies and identification of research gaps
Professional Documentation: Adherence to academic and industry standards for scientific writing

Access the Full Review

📄 Download Complete Literature Review (PDF)

This document is made publicly available to showcase my research and technical writing skills for potential employers and collaborators.

Applications and Impact

The review identifies significant opportunities for LLMs to accelerate drug discovery timelines, reduce development costs, and improve success rates in pharmaceutical research. Key findings highlight the potential for AI-driven approaches to transform traditional drug development paradigms.

This literature review was completed as part of my ongoing research in artificial intelligence applications for pharmaceutical sciences. It represents my commitment to bridging the gap between cutting-edge AI technology and practical healthcare solutions.