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Diffusion models fine-tuned for drug discovery with RL and genotype conditioning

By PulseAugur Editorial · [3 sources] ·

Two new research papers propose advanced methods for using diffusion models in drug discovery. The first, FTDiff, employs reinforcement learning to fine-tune diffusion models for generating molecules with specific drug-like properties and structural compatibility with target proteins. The second paper introduces a genotype-conditioned diffusion model that optimizes molecular candidates based on predicted drug sensitivity, synthesizability, and binding plausibility, grounded in experimental cancer cell line data. AI

IMPACT These methods advance AI's role in accelerating drug discovery by improving the generation of targeted and effective therapeutic molecules.

RANK_REASON Two academic papers published on arXiv proposing novel methods for molecular generation using diffusion models.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Diffusion models fine-tuned for drug discovery with RL and genotype conditioning

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Two academic papers published on arXiv proposing novel methods for molecular generation using diffusion models.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Guang Lin, Shikui Tu, Lei Xu ·

    Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling

    arXiv:2606.01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD). Existing generative approaches, however, often re…

  2. arXiv cs.LG TIER_1 English(EN) · Brenda Nogueira, Gisela A. Gonzalez-Montiel, Nitesh V. Chawla, Nuno Moniz ·

    Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models

    arXiv:2606.01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes. Generative models conditioned on cancer genotypes offer a promisin…

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Nuno Moniz ·

    Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models

    Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes. Generative models conditioned on cancer genotypes offer a promising avenue for personalized drug discovery, yet ex…

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