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Biogen Inc.

Industry researchnorthamerica · us
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Research library5linked papers
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Selected work

Representative Papers

Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance

Apr 27, 2026

This work proposes Pretrained Embedding Distance (PED), a general and tuning-free molecular similarity metric that leverages distances in the embedding space of pretrained molecular models. Traditional similarity measures often rely on handcrafted features or incur high computational costs, while existing deep learning approaches typically require task-specific supervision or large amounts of labeled data, limiting their generalizability. In contrast, PED eliminates the need for both manual feature engineering and task-specific fine-tuning. It effectively ranks active compounds in virtual screening and successfully guides goal-directed molecular generation. Experimental results demonstrate that PED exhibits strong correlation with conventional similarity metrics across multiple tasks, while offering superior scalability and practical utility.

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Iterative Foundation Model Fine-Tuning on Multiple Rewards

Oct 31, 2025

To address the challenge that single-reward signals in foundation model fine-tuning struggle to balance multiple, often conflicting, optimization objectives, this paper proposes MR-ITF—a Multi-Reward Iterative Tuning Framework grounded in reinforcement learning. MR-ITF jointly models heterogeneous structured reward signals (e.g., text fluency, bioactivity, molecular properties), dynamically coordinating gradient updates across objectives in each iteration, and provides theoretical convergence analysis and characterization of training dynamics. Unlike existing RLHF approaches, MR-ITF eliminates the need for manual reward weighting or scalarization, naturally supporting diverse, non-commensurable rewards. Empirically, it achieves state-of-the-art performance across three distinct generative tasks—text generation, protein sequence design, and small-molecule generation—demonstrating superior Pareto-front coverage in multi-objective evaluation and competitive or better single-objective performance. These results validate MR-ITF’s dual advantages in generation quality and objective balancing.

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Recent publications

Latest Papers

Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance

Apr 27, 2026

This work proposes Pretrained Embedding Distance (PED), a general and tuning-free molecular similarity metric that leverages distances in the embedding space of pretrained molecular models. Traditional similarity measures often rely on handcrafted features or incur high computational costs, while existing deep learning approaches typically require task-specific supervision or large amounts of labeled data, limiting their generalizability. In contrast, PED eliminates the need for both manual feature engineering and task-specific fine-tuning. It effectively ranks active compounds in virtual screening and successfully guides goal-directed molecular generation. Experimental results demonstrate that PED exhibits strong correlation with conventional similarity metrics across multiple tasks, while offering superior scalability and practical utility.

0 citationsRead paper

Iterative Foundation Model Fine-Tuning on Multiple Rewards

Oct 31, 2025

To address the challenge that single-reward signals in foundation model fine-tuning struggle to balance multiple, often conflicting, optimization objectives, this paper proposes MR-ITF—a Multi-Reward Iterative Tuning Framework grounded in reinforcement learning. MR-ITF jointly models heterogeneous structured reward signals (e.g., text fluency, bioactivity, molecular properties), dynamically coordinating gradient updates across objectives in each iteration, and provides theoretical convergence analysis and characterization of training dynamics. Unlike existing RLHF approaches, MR-ITF eliminates the need for manual reward weighting or scalarization, naturally supporting diverse, non-commensurable rewards. Empirically, it achieves state-of-the-art performance across three distinct generative tasks—text generation, protein sequence design, and small-molecule generation—demonstrating superior Pareto-front coverage in multi-objective evaluation and competitive or better single-objective performance. These results validate MR-ITF’s dual advantages in generation quality and objective balancing.

0 citationsRead paper