CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation

2026-04-25

AI Summary

Existing methods for dual-target molecule generation often struggle to simultaneously optimize target affinity, physicochemical properties, and synthetic feasibility. This work proposes CombiMOTS, a novel framework that, for the first time, explicitly incorporates synthetic feasibility into dual-target molecular design. By performing Pareto Monte Carlo tree search within a space of synthetically accessible fragments and replacing conventional scalarization with a vectorized multi-objective optimization strategy, CombiMOTS more accurately captures the trade-offs among competing objectives. Experimental results demonstrate that the method generates novel, diverse dual-target candidate molecules with high docking scores and balanced drug-like properties on real-world datasets, thereby validating its effectiveness and practical utility.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Multiagent Systems: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Dual-target molecule generation, which focuses on discovering compounds capable of interacting with two target proteins, has garnered significant attention due to its potential for improving therapeutic efficiency, safety and resistance mitigation. Existing approaches face two critical challenges. First, by simplifying the complex dual-target optimization problem to scalarized combinations of individual objectives, they fail to capture important trade-offs between target engagement and molecular properties. Second, they typically do not integrate synthetic planning into the generative process. This highlights a need for more appropriate objective function design and synthesis-aware methodologies tailored to the dual-target molecule generation task. In this work, we propose CombiMOTS, a Pareto Monte Carlo Tree Search (PMCTS) framework that generates dual-target molecules. CombiMOTS is designed to explore a synthesizable fragment space while employing vectorized optimization constraints to encapsulate target affinity and physicochemical properties. Extensive experiments on real-world databases demonstrate that CombiMOTS produces novel dual-target molecules with high docking scores, enhanced diversity, and balanced pharmacological characteristics, showcasing its potential as a powerful tool for dual-target drug discovery. The code and data is accessible through https://github.com/Tibogoss/CombiMOTS.

Problem

• dual-target molecule generation
• multi-objective optimization
• synthesis-aware
• molecular properties
• target engagement

Innovation

• Pareto Monte Carlo Tree Search
• dual-target molecule generation
• vectorized multi-objective optimization
• synthesis-aware generative model
• fragment-based drug design