Summary

Deep learning is becoming an increasingly powerful tool in the field of small-molecule therapeutics. Its ability to model complex relationships in chemical data is changing the landscape of drug discovery.

One promising application is in the area of antibiotic development. The Chemprop case study demonstrates how deep learning can help identify novel compounds with antibiotic-like properties — an area where traditional approaches often fall short due to high costs and resistance issues.

High-performance computing (HPC) plays a critical role in enabling these advances. Training deep learning models and applying them to massive chemical libraries requires substantial computational resources. Without HPC, it would not be feasible to perform large-scale in-silico screening or optimize these models effectively.

This integration of deep learning and HPC is reshaping how computational drug discovery is conducted.