MolAgent: Biomolecular Property Estimation in the Agentic Era
José Carlos Gómez-Tamayo, Joris Tavernier, Roy Aerts, Natalia Dyubankova, Dries Van Rompaey, Sairam Menon, Marvin N. Steijaert, Jörg K. Wegner, Hugo Ceulemans, Gary Tresadern, Hans L. De Winter, Mazen Ahmad · Journal of Chemical Information and Modeling · 2025
The advent of agentic AI systems is leading to significant transformations across scientific and technological domains. Advances in large language models (LLMs), reasoning capabilities, and integration with external tools have ushered in a new era where agentic AI systems can autonomously perform computational tasks that were traditionally carried out by humans. Computer-aided drug design (CADD)─a multifaceted process encompassing complex, interdependent tasks─stands to benefit profoundly from these advancements. However, one of the key challenges in enabling agentic systems to autonomously take over tasks in CADD is constructing models for property estimation that match the quality and reliability of those developed by human experts. As this is not currently straightforward, this capability represents a major bottleneck for fully realizing the potential of autonomous pipelines in drug discovery. We present here MolAgent, a system-agnostic agentic AI framework designed for high-fidelity modeling of molecular properties in early-stage drug discovery. MolAgent autonomously implements expert-level pipelines for both classification and regression, empowering agentic systems to efficiently construct and deploy models. With integrated automated feature engineering, robust model selection, advanced ensemble methodologies, and comprehensive validation frameworks, MolAgent ensures optimal accuracy and model robustness. The platform seamlessly accepts 2D and 3D structural data for ligands and receptors and harmonizes traditional molecular descriptors with advanced deep learning features extracted from pretrained 2D and 3D encoders. Ultimately, the platform's fully automated, end-to-end workflow is designed for seamless agentic execution. Adherence to the Model Context Protocol (MCP) guarantees interoperability with diverse agentic AI infrastructures, ensuring flexible integration into complex, future discovery pipelines.