Roughness of molecular property landscapes and its impact on modellability

TL;DR

This paper introduces a quantitative measure called ROGI to assess the roughness of molecular property landscapes, which correlates with the difficulty of modeling these landscapes and impacts drug discovery optimization.

Contribution

The paper presents a novel, fractal-inspired roughness index (ROGI) for molecular landscapes, linking geometric complexity to machine learning model performance.

Findings

ROGI correlates with out-of-sample error in regression tasks.

Rougher landscapes pose greater challenges for modeling.

The measure provides insights into activity cliffs and landscape navigability.

Abstract

In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks.