# Latent Molecular Optimization for Targeted Therapeutic Design

arXiv: https://arxiv.org/abs/1809.02032 · HTML: https://mlsanity.com/paper/1809.02032/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2018.

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

- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Multilingual Molecular Representation Learning via Contrastive Pre-training](https://mlsanity.com/paper/2109.08830/) (2021)
- [Exploiting Pretrained Biochemical Language Models for Targeted Drug Design](https://mlsanity.com/paper/2209.00981/) (2022)
- [Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design](https://mlsanity.com/paper/2105.02637/) (2021)
- [AtomSurf : Surface Representation for Learning on Protein Structures](https://mlsanity.com/paper/2309.16519/) (2023)
- [Synthetic Data from Diffusion Models Improve Drug Discovery Prediction](https://mlsanity.com/paper/2405.03799/) (2024)

## Abstract (arXiv)

We devise an approach for targeted molecular design, a problem of interest in computational drug discovery: given a target protein site, we wish to generate a chemical with both high binding affinity to the target and satisfactory pharmacological properties. This problem is made difficult by the enormity and discreteness of the space of potential therapeutics, as well as the graph-structured nature of biomolecular surface sites. Using a dataset of protein-ligand complexes, we surmount these issues by extracting a signature of the target site with a graph convolutional network and by encoding the discrete chemical into a continuous latent vector space. The latter embedding permits gradient-based optimization in molecular space, which we perform using learned differentiable models of binding affinity and other pharmacological properties. We show that our approach is able to efficiently optimize these multiple objectives and discover new molecules with potentially useful binding properties, validated via docking methods.