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Exploring ∆∆G prediction with Siamese Networks
Andrew McNutt · David Koes

During lead optimization, lead molecules are refined for potency via slight modifications of their chemical structure. Relative binding free energy (RBFE) methods allow comparisons of molecular potency during this optimization. We utilize a Siamese Convolutional Neural Network (CNN) to directly estimate the RBFE with higher throughput than simulation based methods. Our models show improved performance over a previously published Siamese RBFE predictor. We observe decreased performance on out-of-domain RBFE predictions.

Author Information

Andrew McNutt (University of Pittsburgh)
David Koes (University of Pittsburgh)

I develop novel computational algorithms and build full-scale systems to support rapid and inexpensive drug discovery while simultaneously applying these methods to develop novel therapeutics. I seek to unlock the power of computation and machine learning to solve challenging, real world problems and am a staunch advocate of open source software and open science.

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