Overview
A top-20 global pharmaceutical company was facing increasing pressure to accelerate their drug discovery process. Traditional high-throughput screening methods were costly and time-consuming, with researchers manually analyzing millions of compounds to identify potential drug candidates.
Koyal Tech Solutions partnered with their R&D division to develop an AI-powered platform that could predict compound efficacy and toxicity, dramatically reducing the number of physical experiments required.
The Challenge
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Massive Data Complexity
Over 50 million compound records with complex molecular structures, biological assay results, and clinical trial data scattered across disparate systems.
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High Failure Rates
95% of compounds that entered preclinical testing failed, representing billions in wasted research investment.
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Time-to-Market Pressure
Competitors were bringing drugs to market faster, and patent cliffs on existing products required accelerated pipeline development.
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Scientific Validation Requirements
Any AI predictions needed to be explainable and validated by medicinal chemists before influencing research decisions.
Our Solution
Unified Data Platform
Built a comprehensive data lake integrating chemical databases, assay results, genomic data, and published research. Implemented graph databases to model complex molecular relationships and protein interactions.
Deep Learning Models
Developed graph neural networks trained on molecular structures to predict binding affinity, ADMET properties (absorption, distribution, metabolism, excretion, toxicity), and off-target effects.
Virtual Screening Engine
Created a high-performance computing pipeline capable of screening 1 million compounds per hour, prioritizing candidates based on predicted efficacy and safety profiles.
Explainable AI Interface
Built an intuitive interface for researchers that visualized model predictions with molecular-level explanations, enabling scientists to understand why compounds were ranked highly.
Implementation Approach
Data Integration (10 weeks)
Consolidated data from 12 internal systems and 20+ external databases into a unified research data platform.
Model Development (16 weeks)
Developed and trained multiple ML models in collaboration with computational chemists, achieving 85% accuracy on held-out test sets.
Validation Studies (12 weeks)
Conducted blind validation studies where AI predictions were tested against wet lab experiments, refining models based on results.
Production Deployment (10 weeks)
Deployed platform to research teams across three global R&D centers with comprehensive training and support.
Results & Impact
5x Faster Screening
Virtual screening reduced the number of physical experiments needed by 80%, accelerating the hit-to-lead phase significantly.
$12M Annual Savings
Reduced wet lab costs and accelerated timelines translated to significant R&D budget savings.
3 New Drug Candidates
Platform identified 3 promising compounds that advanced to preclinical trials within the first year of deployment.
200+ Researchers Enabled
Platform adopted by research teams across oncology, immunology, and neuroscience therapeutic areas.
"This platform has fundamentally changed how our researchers approach drug discovery. We're now able to explore chemical space that would have been impossible with traditional methods."
