# Combating small molecule aggregation with machine learning

arXiv: https://arxiv.org/abs/2105.00267 · HTML: https://mlsanity.com/paper/2105.00267/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 64 references that are in the map.
- First posted on arXiv 1 May 2021.

## Builds on (most cited first, 3 of 3)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://mlsanity.com/paper/1811.12823/) (2018)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)

## Built on by (most cited first, 1 of 1)

- [Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI](https://mlsanity.com/paper/2306.02206/) (2023)

## Abstract (arXiv)

Biological screens are plagued by false positive hits resulting from aggregation. Thus, methods to triage small colloidally aggregating molecules (SCAMs) are in high demand. Herein, we disclose a bespoke machine-learning tool to confidently and intelligibly flag such entities. Our data demonstrate an unprecedented utility of machine learning for predicting SCAMs, achieving 80% of correct predictions in a challenging out-of-sample validation. The tool outperformed a panel of expert chemists, who correctly predicted 61 +/- 7% of the same test molecules in a Turing-like test. Further, the computational routine provided insight into molecular features governing aggregation that had remained hidden to expert intuition. Leveraging our tool, we quantify that up to 15-20% of ligands in publicly available chemogenomic databases have the high potential to aggregate at typical screening concentrations, imposing caution in systems biology and drug design programs. Our approach provides a means to augment human intuition, mitigate attrition and a pathway to accelerate future molecular medicine.