Institution profile

Sanofi

Industry researcheurope · fr
Official website
Research library13linked papers
Opportunities0open roles
Selected work

Representative Papers

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

Jul 14, 2026

This work addresses the challenge in structure-based drug design where existing diffusion models struggle to simultaneously optimize target binding affinity and molecular developability, particularly ADMET properties. The authors propose conDitar-dev, a novel framework that decouples pocket and ligand representations and integrates multi-scale pocket encoding (msPRL), pocket-conditioned diffusion generation (conDitar), and a property-aware optimization strategy (paOPT) to enable atomic- and molecular-level co-modeling with real-time ADMET optimization during generation. Evaluated on human disease targets, the method achieves an average binding energy of −8.85 kcal/mol and improves ADMET performance by up to 73%. Experimentally validated molecules targeting PD-L1 and CSF1R demonstrate micromolar binding affinity and nanomolar inhibitory activity.

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Potential Role of Agentic Artificial Intelligence in Toxicologic Pathology

Jan 26, 2026

As the volume and complexity of nonclinical toxicology studies continue to increase, toxicologic pathology reporting faces persistent challenges, including fragmented sources of data (e.g., histopathology images, clinical pathology and other study data, adverse effects database, mechanistic literature), variable reporting timelines and heightened regulatory expectations. This white paper examines the emerging role of agentic artificial intelligence (AI) in addressing these issues through coordinated workflow orchestration, data integration, and pathologist-in-the-loop report generation. Based on a closed-door roundtable held during the 2025 Society of Toxicologic Pathology (STP) Annual Meeting and follow-on discussions, this paper synthesizes the perspectives of leading toxicologic pathologists, toxicologists, and AI developers. It outlines the key pain points in current reporting workflows, identifies realistic near-term use cases for agentic AI, and describes major adoption barriers including requirements for transparency, validation, and organizational readiness. A phased adoption roadmap and pilot design considerations are proposed to help support responsible evaluation and deployment of agentic AI system in nonclinical settings. The paper concludes by emphasizing the need for coordinated efforts across pharmaceutical organizations, CROs, academia, and regulators to establish shared standards, benchmarks, and governance frameworks that will lead to safe, transparent, and trustworthy integration of AI into toxicologic science.

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Solvaformer: an SE(3)-equivariant graph transformer for small molecule solubility prediction

Nov 12, 2025

To address the strong reliance on quantum descriptors, poor interpretability, and high experimental costs in small-molecule solubility prediction, this work proposes a geometry-aware SE(3)-equivariant graph neural network. The model integrates SE(3)-equivariant attention with scalar attention in a synergistic architecture to enable geometrically faithful intermolecular communication without spurious relative geometric constraints. A multi-task alternating training strategy jointly leverages quantum-mechanical computations and experimental solubility data, enhancing both generalization and attribution-based interpretability. On multiple benchmarks, our method matches the performance of DFT-augmented gradient-boosting approaches, significantly outperforms ablated EquiformerV2 variants and sequence-based models, and—through attention visualization—reveals key solvation mechanisms such as hydrogen bonding. This provides a high-accuracy, interpretable predictive tool for molecular synthesis and process optimization.

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Recent publications

Latest Papers

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

Jul 14, 2026

This work addresses the challenge in structure-based drug design where existing diffusion models struggle to simultaneously optimize target binding affinity and molecular developability, particularly ADMET properties. The authors propose conDitar-dev, a novel framework that decouples pocket and ligand representations and integrates multi-scale pocket encoding (msPRL), pocket-conditioned diffusion generation (conDitar), and a property-aware optimization strategy (paOPT) to enable atomic- and molecular-level co-modeling with real-time ADMET optimization during generation. Evaluated on human disease targets, the method achieves an average binding energy of −8.85 kcal/mol and improves ADMET performance by up to 73%. Experimentally validated molecules targeting PD-L1 and CSF1R demonstrate micromolar binding affinity and nanomolar inhibitory activity.

0 citationsRead paper

Potential Role of Agentic Artificial Intelligence in Toxicologic Pathology

Jan 26, 2026

As the volume and complexity of nonclinical toxicology studies continue to increase, toxicologic pathology reporting faces persistent challenges, including fragmented sources of data (e.g., histopathology images, clinical pathology and other study data, adverse effects database, mechanistic literature), variable reporting timelines and heightened regulatory expectations. This white paper examines the emerging role of agentic artificial intelligence (AI) in addressing these issues through coordinated workflow orchestration, data integration, and pathologist-in-the-loop report generation. Based on a closed-door roundtable held during the 2025 Society of Toxicologic Pathology (STP) Annual Meeting and follow-on discussions, this paper synthesizes the perspectives of leading toxicologic pathologists, toxicologists, and AI developers. It outlines the key pain points in current reporting workflows, identifies realistic near-term use cases for agentic AI, and describes major adoption barriers including requirements for transparency, validation, and organizational readiness. A phased adoption roadmap and pilot design considerations are proposed to help support responsible evaluation and deployment of agentic AI system in nonclinical settings. The paper concludes by emphasizing the need for coordinated efforts across pharmaceutical organizations, CROs, academia, and regulators to establish shared standards, benchmarks, and governance frameworks that will lead to safe, transparent, and trustworthy integration of AI into toxicologic science.

0 citationsRead paper

Solvaformer: an SE(3)-equivariant graph transformer for small molecule solubility prediction

Nov 12, 2025

To address the strong reliance on quantum descriptors, poor interpretability, and high experimental costs in small-molecule solubility prediction, this work proposes a geometry-aware SE(3)-equivariant graph neural network. The model integrates SE(3)-equivariant attention with scalar attention in a synergistic architecture to enable geometrically faithful intermolecular communication without spurious relative geometric constraints. A multi-task alternating training strategy jointly leverages quantum-mechanical computations and experimental solubility data, enhancing both generalization and attribution-based interpretability. On multiple benchmarks, our method matches the performance of DFT-augmented gradient-boosting approaches, significantly outperforms ablated EquiformerV2 variants and sequence-based models, and—through attention visualization—reveals key solvation mechanisms such as hydrogen bonding. This provides a high-accuracy, interpretable predictive tool for molecular synthesis and process optimization.

0 citationsRead paper