PMO-Dock: Benchmarking Docking, Specificity and Generalization in Molecular Optimization

Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan, Tigran Fahradyan, Hrant Khachatrian

Keywords: Benchmark, Drug Discovery, ML, Molecular optimization

Abstract:

The Practical Molecular Optimization (PMO) benchmark standardized evaluation
in the field, however, recent structure-based methods have drifted from its rigorous
spirit by overfitting hyperparameters and prompts to specific test targets. To address
this, we propose PMO-Dock, a new benchmark and protocol that enforces strict
generalization: algorithms must tune hyperparameters on a validation set of diverse
protein targets and freeze them for a distinct test set. We introduce 23 tasks covering
hit generation, lead optimization, and novel “docking with specificity” challenges
that penalize off-target binding. We benchmark four diverse high-performing
methods spanning different optimization paradigms: Saturn (reinforcement learn-
ing), GenMol (discrete diffusion), Genetic-guided GFlowNet, and Chemlactica
(LLM-based). Our extensive analysis reveals that hyperparameters maximizing
performance on validation targets rarely transfer to test tasks, highlighting a critical
fragility in current state-of-the-art methods. Our PMO-Dock supports task-aware
hyperparameter selection without test-set overfitting, providing a robust foundation
for the next generation of generalizable molecular optimizers.

Presenter: ~Tatevik_Abrahamyan1

Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 107