MADD : Multi-Agent Drug Discovery Orchestra (2025)

Abstract: Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design.

## introduction identifying hit molecules is at the core of drug discovery @xcite . hits are commonly defined as chemical compounds with several desired properties, such as biological activity against a particular target associated with a disease, stability, synthetic accessibility, and others @xcite . with significant advances and widespread of artificial intelligence (ai) in the * glebsolo46@gmail.com † dmitrenko@pish.itmo.ru ‡ anna.kalyuzhnaya@itmo.ru last decade, virtual screening has become an essential part of drug discovery @xcite . ai methods for hit identification aim to reduce the resources required for high-throughput wet-lab experimentation and improve their success rate. significant progress has been made in the adoption of such methods in the pharmaceutical industry all around the world @xcite . however, the complexity of ai methods has also grown dramatically, resulting in limited accessibility of these tools for wet-lab researchers to pursue their drug discovery hypotheses @xcite . llms have emerged as powerful tools for bridging this gap, offering impressive capabilities for interpreting natural language descriptions of desired molecular properties and generating candidate molecules @xcite . several promising llm-based tools have been proposed recently, including drugllm @xcite for zero-shot molecular generation, chem-llm @xcite for chemistry-related q&a, llasmol @xcite for molecular property prediction, and x-lora-gemma (stewart and buehler, 2024) for molecular optimization. however, llms alone are not competitive in molecule generation and molecular property prediction specific to early drug discovery. combining an llm with highly specialized tools gave rise to agentic architectures. the first examples, such as chemagent @xcite , chemcrow (m. @xcite , and cactus @xcite , demonstrated a high potential of this approach, but also highlighted the challenges of integrating numerous ai-based tools and automatic assembly of effective processing pipelines. finally, multi-agent systems have been introduced @xcite @xcite . we were wondering whether a multi-agent system could, in principle, be an effective end-to-end solution for hit identification in early drug discovery and overcome limitations of existing solutions by providing comprehensive execution of all stages: semantic query analysis, target-adaptive molecule generation, property calculation (binding affinity, synthetic accessibility), which is particularly crucial for drug discovery tasks. more specifically, we formulated the following research hypothesis: multi-agent architecture, by distributing complex tasks across specialized agents with distinct roles, can significantly outperform both singleagent approaches and pure llm generators in automating the complete drug discovery workflow through improved task coordination and domainspecific expertise (through domain pipelines and tools) integration. to pursue this hypothesis, we developed madd, a multi-agent drug discovery orchestra that employs four specialized agents to coordinate and execute hit identification subtasks. to inform the architecture design choices and validate the system's overall performance, we formulated six drug discovery cases related to the following conditions: alzheimer's disease, parkinson's disease, multiple sclerosis, lung cancer, dyslipidemia, and drug resistance. for each condition, we assembled a unique dataset, trained machine learning models, and implemented logic to validate the generated molecules. we compared madd to existing llmbased solutions and demonstrated its superior performance. as additional validation case studies, we compared the molecules produced by madd with experimentally validated gsk-3β inhibitors and applied madd to a recently published (and, therefore, unseen) case of thrombocytopenia. our empirical results demonstrate that madd is one of the most effective multi-agent solutions for hit identification in drug discovery to date. in summary, the contributions of this paper are: the results of our study are open source (code and data): ## related work recent advances highlight the growing role of llms in chemistry, including predictive modeling, compound generation, and drug discovery. chemdfm @xcite , llasmol @xcite , x-lora-gemma @xcite , and cancergpt @xcite exemplify applications in data analysis, molecular optimization, and synergy prediction. ye @xcite proposed a de novo molecule generation method, and m. @xcite showed improved molecular property predictions by integrating llms with chemical tools. agent-based llm frameworks such as drugagent @xcite , chemcrow (m. @xcite , cac-tus @xcite , and chemagent @xcite automate various steps in chemical research, from synthesis planning to molecular property analysis. broader surveys @xcite confirm the efficiency and scalability of these systems. these studies highlight that llms can accelerate scientific progress in chemistry and related fields, unlocking new material and drug development opportunities. nevertheless, none demonstrate successful automation of the whole drug discovery pipeline. possible reasons are (1) the absence of a readily accessible dataset with a complete pipeline for training and validating new models and approaches, and (2) the weakness of existing separated models and agent architectures for solving the whole task at a high-quality level. based on these conclusions, we propose our vision of a stronger approach and a valuable newly farmed dataset with state-of-the-art pharmaceutical research tasks and their solutions. we set out to develop a system that harnesses advanced drug design methodologies and enhances their accessibility to domain experts by integrating llm-based agents. these agents can autonomously operate molecular generation and property prediction tools, enabling more efficient and systematic hypothesis testing for identifying promising drug candidates. this strategy aims to significantly reduce research costs while allowing human experts to focus on more complex and valuecreating aspects of the pharmaceutical development process. to this end, we introduce the madd approach, which integrates various generative and predictive tools into a coherent, end-to-end drug discovery pipeline. the system is built upon a multi-agent architecture (figure 1 ) specifically designed to manage the complexity of automating early drug discovery workflows. ## madd orchestra the system's architecture isolates key functions in specialized agents, each optimized for specific tasks in the overall workflow. we define four agents and two llm-tools to achieve high operational accuracy and ground their roles in modular software design principles, reducing the space of possible actions for each agent. specialization of agents has at least two advantages: ## experimental studies in this section, we present a series of experimental results that informed the design of madd architecture and proved the overall effectiveness of our approach. ## automl features of madd we evaluated the effectiveness of madd's automatic training generative model by comparing it to manual training on alzheimer's disease. predictive performance was assessed across all six diseases. predictive models were used in the automatic learning process of generative models to stop learning early and select the best learning epoch, considering the value of the loss function and the error in matching the required properties in the generated molecules. as shown in figure 4 , by automatically running the automl framework, madd can train machine learning pipelines for molecular property prediction tasks that outperform pre-trained manual models in most cases. the efficiency of generative models trained automatically by madd is comparable in terms of the number of target molecules generated and the average docking score on the alzheimer's disease example. as a result, the study significantly outperformed the average docking score in terms of the percentage of target molecules generated. the results of training the generative models are shown in additionally, we evaluated whether the tool selection (ts) metric is retained when adding a tool for running generative or predictive model training. as a result of this evaluation, tool selection accuracy decreased slightly from 83.7% ( ## case studies alzheimer's disease case study. we conducted a case study on alzheimer's disease to compare molecules generated by madd with experimentally validated gsk-3β inhibitors from chembl (figure 5a ). the madd-generated molecules exhibited higher mean sa and qed scores compared to the chembl compounds. in addition, the madd molecules showed improved binding affinities to the target protein. moreover, generated compounds demonstrated high structural diversity, with an average tanimoto similarity of 0.43. this suggests that madd can be an effective tool for de novo drug design, potentially producing candidates that surpass existing ligands in terms of key drug-relevant properties. figure 5 : results of case studies: a). alzheimer's disease case study; "chembl" -experimentally validated gsk-3β inhibitors from chembl, "madd"molecules generated by our approach. b). thrombocytopenia case study; "syk-fbrl" -molecules generated by syk-fbrl approach, "madd" -molecules generated by our approach. thrombocytopenia case study. this study involved validation using one of the recently explored drug design cases on thrombocytopenia @xcite , where fragment-based rl was employed for the generation of novel syk-inhibitors (hereafter, syk-fbrl). madd was provided the dataset containing ∼3.2k sykinhibitors to train new generative and predictive models for this case automatically. using the automl framework, madd could derive a pipeline to predict pic50, achieving a metric value of r 2 =0.75, compared to 0.78 in syk-fbrl. more details about the resulting machine learning pipelines are described in the appendix c.7 besides the predictive models for pic50 and docking score, madd managed to fine-tune the transformer model. ultimately, madd generated 10k molecules, of which 132 matched all five of our property filters. in contrast, the original syk-fbrl approach generated 76k molecules, of which 139 corresponded to the required pic50 and ds properties. this outcome highlights the superior efficiency of madd in hit identification. additionally, figure 5b compares maddgenerated molecules with compounds produced by syk-fbrl. the fully automatic pipeline of madd resulted in molecules with improved docking scores, qed, and sa properties. notably, these results were obtained without any adjustments to madd configuration and with no additional inputs from human experts (e.g., medicinal chemists) @xcite . this outcome is the most substantial evidence in support of the hypothesis formulated in this work. it also highlights the excellent potential for madd to streamline and enhance early-stage drug discovery. ## discussion while madd demonstrates promising generalization capabilities in the thrombocytopenia case, which was not part of the original design, several limitations regarding its applicability to new cases should be further explored. in its current implementation, users are required to provide their own dataset of molecules and corresponding target properties to train madd's generative and predictive tools. in practice, many users may lack access to well-curated datasets, impacting both model performance and reproducibility. moreover, madd's pipeline assumes prior knowledge of the biological target or relevant properties, which limits the discovery of ligands for fundamentally novel targets. to address these issues, future work should focus on integrating automated data curation pipelines and incorporating modules for hypothesis generation to enable more open-ended discovery. additionally, the system's current generalization is predominantly demonstrated in silico without broader experimental validation. we tried to ensure in silico validation of generated hits. however, translating these predictions into real-world outcomes requires experimental validation through biochemical assays, cell-based screens, and animal studies, which are inherently time-consuming, costly, and resource-intensive processes. this imbalance creates a bottleneck: while ai systems can explore chemical space at unprecedented scale and speed, only a small fraction of candidates can realistically be tested in the lab. nevertheless, future efforts will focus on extending this work through experimental collaborations to confirm the predicted activities and refine the model based on empirical results. integrating wet lab validation into the madd pipeline would strengthen the biological relevance of its outputs and enable constant iterative improvement, further bridging the gap between ai-driven design and practical drug development. ## conclusion this paper introduces madd, a multi-agent system tackling the challenge of hit identification. madd coordinates multiple tools to design molecules and assess their properties, achieving 79.8% overall pipeline accuracy. madd consistently outperforms baselines in hit identification efficiency, yielding up to 6.63% novel hits that meet eight key drug criteria. validation on two case studies, including an unseen thrombocytopenia case, further confirms its autonomous discovery capabilities. we contribute to the future of drug design agents by presenting a novel benchmark and discussing current generalization and wet lab experimentation challenges.