Abstract Machine learning (ML) systems have enabled the modelling of quantitative structure–property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integrates active learning (AL) and iterative feedback to refine property predictors, thereby improving the outcomes of their optimization by generative AI agents. Our method leverages the Expected Predictive Information Gain (EPIG) criterion to select additional molecules for evaluation by an oracle. This process aims to provide the greatest reduction in predictive uncertainty, enabling more accurate model evaluations of subsequently generated molecules. Recognizing the impracticality of immediate wet-lab or physics-based experiments due to time and logistical constraints, we propose leveraging human experts for their cost-effectiveness and domain knowledge to effectively augment property predictors, bridging gaps in the limited training data. Empirical evaluations through both simulated and real human-in-the-loop experiments demonstrate that our approach refines property predictors to better align with oracle assessments. Additionally, we observe improved accuracy of predicted properties as well as improved drug-likeness among the top-ranking generated molecules. Scientific contribution We present an adaptable framework that integrates AL and human expertise to refine property predictors for goal-oriented molecule generation. This approach is robust to noise in human feedback and ensures that navigating chemical space with human-refined predictors leverages human insights to identify molecules that not only satisfy predicted property profiles but also score highly on oracle models. Additionally, it prioritizes practical characteristics such as drug-likeness, synthetic accessibility, and a favorable balance between exploring diverse chemical space and exploiting similarity to existing training data.

Yasmine Nahal

Janosch Menke

Julien Martinelli

Markus Heinonen

Mikhail Kabeshov

Jon Paul Janet

Eva Nittinger

Ola Engkvist

Samuel Kaski

Journal of Cheminformatics

JOURNAL OF CHEMINFORMATICS 是一本开放获取期刊，发表化学信息学和分子建模各个方面的原始同行评议研究。覆盖范围包括但不限于：化学信息系统、软件和数据库，以及分子建模化学结构表示及其在结构中的应用、化学物质和化学反应数据库的子结构和相似性搜索计算机和分子图形、计算机辅助分子设计、专家系统、QSAR 和数据挖掘技术。

The Journal of Cheminformatics is an open-access journal dedicated to original peer-reviewed research in all aspects of cheminformatics and molecular modeling. Its scope includes, but is not limited to: chemical information systems, software and databases, molecular modeling of chemical structure representations and their applications, substructure and similarity searching in chemical substance and reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR (Quantitative Structure-Activity Relationship), and data mining techniques.

《Journal of Cheminformatics》是一本开放获取的期刊，专注于化学信息学和分子建模领域的原创性同行评审研究。期刊的覆盖范围包括但不限于：化学信息系统、软件和数据库，以及分子建模中的化学结构表示及其应用、化学物质和化学反应数据库的子结构和相似性搜索、计算机和分子图形、计算机辅助分子设计、专家系统、QSAR（定量构效关系）和数据挖掘技术。

Category	Quartile
化学	2
化学, 化学综合	2
化学, 计算机信息系统	2
化学, 计算机跨学科应用	3

Human-in-the-loop active learning for goal-oriented molecule generation

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	1