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New generative models accelerate molecular design for drug discovery · 6 sources tracked

By PulseAugur Editorial · [7 sources] ·

Researchers have developed several advanced generative models for molecular design, focusing on precision and efficiency. JoPMol integrates gene expression data with molecular structure and properties for personalized drug candidate discovery. ConDitar-dev utilizes pocket-conditioned diffusion and property optimization to generate molecules with strong binding affinities and favorable ADMET properties, demonstrating experimental success in identifying drug candidates. SEGO, a sample-efficient Bayesian optimization framework, significantly reduces the number of evaluations needed to find promising molecules, moving molecular optimization closer to direct experimental feedback. AI

IMPACT These advancements in generative AI are accelerating drug discovery and materials science by enabling more efficient and precise molecular design.

RANK_REASON Multiple arXiv papers detailing new generative models and optimization frameworks for molecular design.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 7 sources. How we write summaries →

New generative models accelerate molecular design for drug discovery · 6 sources tracked

COVERAGE [7]

  1. arXiv cs.LG TIER_1 English(EN) · Ruoxi Gao, Jiangweizhi Peng, Ziqi Chen, Frazier N. Baker, David C. Kombo, John L. Kane Jr., Andrew A. Scholte, Yi Li, Matthew J. LaMarche, Luigi I. Iconaru, Hans-Peter Biemann, Mingyi Hong, Xia Ning ·

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

    arXiv:2607.12349v1 Announce Type: new Abstract: Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models follow the structure-based drug design (SBDD) paradigm, …

  2. arXiv cs.AI TIER_1 English(EN) · Hang Yuan, Chen Li, Wenjun Ma, Tadahiko Murata, Yuncheng Jiang ·

    Gene Expression-Informed Jointly Controlled Generative Modeling for Precision Molecular Design

    arXiv:2607.11978v1 Announce Type: cross Abstract: Precision molecular design aims to discover personalized drug candidates through joint control of multiple conditions, such as biological relevance and molecular design strategies. Biological relevance reflects cellular functional…

  3. arXiv cs.LG TIER_1 English(EN) · Sarina Kopf, Cristina Nevado, Philippe Schwaller ·

    Sample Efficient Generative Optimization for Molecular Design

    arXiv:2607.12488v1 Announce Type: new Abstract: Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly. The practical im…

  4. arXiv cs.LG TIER_1 English(EN) · Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen ·

    MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design

    arXiv:2411.06608v3 Announce Type: replace Abstract: We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design. MolMiner supports high-dimensional conditional control over twelve physicochemical and structural properties …

  5. arXiv cs.LG TIER_1 English(EN) · Philippe Schwaller ·

    Sample Efficient Generative Optimization for Molecular Design

    Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly. The practical impact of an optimization method therefore hinges …

  6. arXiv cs.LG TIER_1 English(EN) · Xia Ning ·

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

    Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models follow the structure-based drug design (SBDD) paradigm, generating molecules to fit a target binding poc…

  7. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models follow the structure-based drug design (SBDD) paradigm, generating molecules to fit a target binding poc…

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