Enterprise AI Analysis
How evaluation choices distort the outcome of generative drug discovery
Discovering new therapeutics is an adventure as old as human civilization. However, finding new drug molecules is more resource-intensive today than ever [1, 2]. A key challenge lies in the vastness of the 'chemical universe,' which is estimated to contain more than 1060 drug-like molecules where compounds with desirable biological properties are exceedingly rare [3]. Artificial intelligence (AI) has emerged as a transformative technology for drug discovery, to help find the 'needle in the haystack.' By supporting virtual screening [4-6] and de novo molecule design [7-12], AI can narrow down the chemical universe, and it is nowadays widely adopted in academia and industry [13-17]. Generative deep learning has garnered particular attention for drug discovery. Powered by deep neural networks, these models can learn how to generate molecules with desired properties on demand, and have already demonstrated success in prospective studies [7, 18-22].
Executive Impact: Key Metrics
Understand the scale and implications of generative deep learning in drug discovery.
1060
Drug-like Molecules in Chemical Universe
3x
Drug Discovery Stages (Train, Generate, Evaluate)
1 Billion
Molecules Analyzed in This Study
Deep Analysis & Enterprise Applications
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100,000+
Designs for Reliable Evaluation
Enterprise Process Flow
Train Model
→
Generate Molecules
→
Evaluate Designs
→
Refine & Iterate
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| Diversity Metrics |
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| Molecule Selection |
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| Sampling Strategy |
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Overcoming the 'Size Trap'
Our analysis reveals a 'size trap' where the number of generated designs significantly impacts evaluation outcomes, leading to misleading model comparisons. For instance, Frechét ChemNet Distance (FCD) values only plateau and stabilize when more than 10,000 designs are considered. This highlights that many current benchmarks using smaller libraries might be providing an inaccurate assessment of model performance. By generating and evaluating approximately 1 billion molecular designs, we demonstrate that increasing library size is crucial for robust and reliable generative modeling evaluation, especially for metrics like distributional similarity and diversity.
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