# Pareto Optimization to Accelerate Multi-Objective Virtual Screening

arXiv: https://arxiv.org/abs/2310.10598 · HTML: https://mlsanity.com/paper/2310.10598/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 75 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [Uncertainty Quantification Using Neural Networks for Molecular Property Prediction](https://mlsanity.com/paper/2005.10036/) (2020)
- [Accelerating high-throughput virtual screening through molecular pool-based active learning](https://mlsanity.com/paper/2012.07127/) (2020)
- [DOCKSTRING: easy molecular docking yields better benchmarks for ligand design](https://mlsanity.com/paper/2110.15486/) (2021)
- [Computer-Aided Multi-Objective Optimization in Small Molecule Discovery](https://mlsanity.com/paper/2210.07209/) (2022)
- [Discovering Many Diverse Solutions with Bayesian Optimization](https://mlsanity.com/paper/2210.10953/) (2022)
- [New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization](https://mlsanity.com/paper/2210.01071/) (2022)
- [Self-focusing virtual screening with active design space pruning](https://mlsanity.com/paper/2205.01753/) (2022)
- [An Analysis of Quality Indicators Using Approximated Optimal Distributions in a Three-dimensional Objective Space](https://mlsanity.com/paper/2009.12788/) (2020)

## Built on by (most cited first, 6 of 6)

- [Bayesian optimization as a flexible and efficient design framework for sustainable process systems](https://mlsanity.com/paper/2401.16373/) (2024)
- [Preferential Multi-Objective Bayesian Optimization for Drug Discovery](https://mlsanity.com/paper/2503.16841/) (2025)
- [Beyond Regrets: Geometric Metrics for Bayesian Optimization](https://mlsanity.com/paper/2401.01981/) (2024)
- [A study of EHVI vs fixed scalarization for molecule design](https://mlsanity.com/paper/2507.13704/) (2025)
- [AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture](https://mlsanity.com/paper/2601.03284/) (2026)
- [Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits](https://mlsanity.com/paper/2606.18785/) (2026)

## Abstract (arXiv)

The discovery of therapeutic molecules is fundamentally a multi-objective optimization problem. One formulation of the problem is to identify molecules that simultaneously exhibit strong binding affinity for a target protein, minimal off-target interactions, and suitable pharmacokinetic properties. Inspired by prior work that uses active learning to accelerate the identification of strong binders, we implement multi-objective Bayesian optimization to reduce the computational cost of multi-property virtual screening and apply it to the identification of ligands predicted to be selective based on docking scores to on- and off-targets. We demonstrate the superiority of Pareto optimization over scalarization across three case studies. Further, we use the developed optimization tool to search a virtual library of over 4M molecules for those predicted to be selective dual inhibitors of EGFR and IGF1R, acquiring 100% of the molecules that form the library's Pareto front after exploring only 8% of the library. This workflow and associated open source software can reduce the screening burden of molecular design projects and is complementary to research aiming to improve the accuracy of binding predictions and other molecular properties.