MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
arXiv:2603.18256v1 Announce Type: new Abstract: Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex problem-solving tasks. Motivated by these advances, several works have explored the application of reasoning LLMs to drug discovery and molecular design. However, most existing approaches either focus on evaluation or rely on training setups that require ground-truth labels, such as molecule pairs with known property modifications. Such supervision is unavailable in \textit{de novo} molecular generation, where the objective is to generate novel molecules that optimize a desirability score without prior knowledge of high-scoring candidates. To bridge this gap, we introduce MolRGen, a large-scale benchmark and dataset for training and evaluating reasoning-based LLMs on \textit{de novo} molecular generation. Our contributions are threefold. First, we propose a setting to evaluate and train models for \textit{de novo} molecul
arXiv:2603.18256v1 Announce Type: new Abstract: Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex problem-solving tasks. Motivated by these advances, several works have explored the application of reasoning LLMs to drug discovery and molecular design. However, most existing approaches either focus on evaluation or rely on training setups that require ground-truth labels, such as molecule pairs with known property modifications. Such supervision is unavailable in \textit{de novo} molecular generation, where the objective is to generate novel molecules that optimize a desirability score without prior knowledge of high-scoring candidates. To bridge this gap, we introduce MolRGen, a large-scale benchmark and dataset for training and evaluating reasoning-based LLMs on \textit{de novo} molecular generation. Our contributions are threefold. First, we propose a setting to evaluate and train models for \textit{de novo} molecular generation and property prediction. Second, we introduce a novel diversity-aware top-$k$ score that captures both the quality and diversity of generated molecules. Third, we show our setting can be used to train LLMs for molecular generation, training a 24B LLM with reinforcement learning, and we provide a detailed analysis of its performance and limitations.
Executive Summary
The article introduces MolRGen, a benchmark and dataset for training and evaluating reasoning-based large language models (LLMs) on de novo molecular generation. This setting addresses the limitation of existing approaches that rely on training setups requiring ground-truth labels. MolRGen proposes a novel diversity-aware top-k score and demonstrates the ability to train a 24B LLM using reinforcement learning. The analysis provides insights into the model's performance and limitations, contributing to the advancement of reasoning-based LLMs in molecular design.
Key Points
- ▸ MolRGen is a benchmark and dataset for de novo molecular generation and property prediction.
- ▸ The setting introduces a novel diversity-aware top-k score to capture quality and diversity of generated molecules.
- ▸ A 24B LLM is successfully trained using reinforcement learning, demonstrating the feasibility of MolRGen.
Merits
Comprehensive Approach
MolRGen addresses the limitation of existing approaches by providing a comprehensive setting for training and evaluation, including a novel diversity-aware top-k score and a large-scale benchmark.
State-of-the-Art Model Performance
The successful training of a 24B LLM using reinforcement learning demonstrates the potential of MolRGen to achieve state-of-the-art model performance in de novo molecular generation.
Demerits
Limited Generalizability
The article primarily focuses on a specific type of molecular generation and property prediction, which may limit the generalizability of MolRGen to other domains or tasks.
Scalability Challenges
The large-scale benchmark and dataset may pose scalability challenges for researchers and practitioners, particularly those with limited computational resources.
Expert Commentary
The introduction of MolRGen represents a significant contribution to the field of molecular design, leveraging the advances in reasoning-based large language models. The novel diversity-aware top-k score and the successful training of a 24B LLM using reinforcement learning demonstrate the potential of MolRGen to achieve state-of-the-art model performance. However, the article's focus on a specific type of molecular generation and property prediction may limit its generalizability. Nevertheless, MolRGen has the potential to accelerate the development of de novo molecular generation and property prediction in the pharmaceutical industry, making it a valuable resource for researchers and practitioners.
Recommendations
- ✓ Future research should aim to extend the generalizability of MolRGen to other domains and tasks in molecular design.
- ✓ The development of scalable and accessible versions of MolRGen would facilitate its adoption in the pharmaceutical industry and beyond.
