Good Papers: read the good ones

PGLD optimizes synthesis-aware stochastic DNA libraries via policy gradients to bypass synthesis cost limits, enabling million-sequence libraries for antibody exploration at low cost.

Scott Sussex, Ema Borevković, Frederieke Lohmann, Ningning Chen and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Michigan - Ann Arbor Michigan State Molecules & drug discovery

Chain-of-Generation: Progressive Latent Diffusion for Text-Guided Molecular Design

Chain-of-Generation progressively decomposes prompts into curriculum-ordered segments to guide multi-stage latent diffusion, improving text-aligned molecular design with greater controllability and interpretability.

Lingxiao Li, Haobo Zhang, Bin Chen, Jiayu Zhou

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026The University of Hong Kong Immunocan Biotech Peking International Digital Economy Ac International Digital Economy Ac Molecules & drug discovery

Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization

SGRPO directly rewards set-level diversity via leave-one-out contributions in a flexible GRPO framework, expanding the utility-diversity Pareto frontier across biomolecular design tasks.

Xinwu Ye, He CAO, Li Hao, Bin Feng and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face · Code ★ 3

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lenient 5/5

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NeurIPS 2026Massachusetts Institute of Techn Shanghai Jiao Tong MIT Molecules & drug discovery

GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem

GLACIER treats tandem mass spectrum prediction as graph object detection, outperforming prior state-of-the-art by up to 19.3% on retrieval accuracy with nearly 8-fold faster inference.

Rui-Xi Wang, Runzhong Wang, Connor Coley

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

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NeurIPS 2026SpotlightU Zurich Ecole Polytechnique Paris / Tech Graz University of Technology Molecules & drug discovery

Generating Physically Consistent Molecules with Energy-Based Models

EBMol learns atom-additive scalar potentials via flow-inspired restoring field matching to generate physically consistent 3D molecules with state-of-the-art results.

Christoph Griesbacher, Lea Bogensperger, Andreas Habring, Thomas Pock

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 3/5

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NeurIPS 2026KAIST Korea Advanced Institute of Scie Korea Advanced Institute of Scie Molecules & drug discovery

Screening Lipid Nanoparticles through Structure-Ratio Alignment

STRATA predicts lipid nanoparticle transfection by aligning molecular structure and composition ratio representations to model component interactions. It improves accuracy and generalizes to unseen molecules and ratios.

Yoonho Lee, Yunhak Oh, Hoyoung Choi, Chanyoung Park

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Hong Kong University of Science The Hong Kong University of Scie Beijing Institute of Mathematica Molecules & drug discovery

Probe Before You Edit: Probing-Guided Molecular Optimization for LLM Agents in Structure-Based Drug Design

PROBE uses edit-response probing to build pocket-specific site maps and EditManuals that guide multi-agent optimization of both affinity and druggability in structure-based drug design, achieving state-of-the-art on CrossDocked2020.

Zaifei Yang, Weiyu Chen, Yaqing Wang, James Kwok

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Microsoft Microsoft Research Molecules & drug discovery

MorphoHELM: A Comprehensive Benchmark for Evaluating Representations for Microscopy-Based Morphology Assays

MorphoHELM benchmarks microscopy representation methods across batch effects, finding classic computer vision strategies outperform deep learning across settings and revealing trade-offs between models.

Emre Hayir, Lorin Crawford, Alex X Lu

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

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NeurIPS 2026ETHZ - ETH Zurich ETH Zurich Molecules & drug discovery

Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport

Morph is a flexible-size generative model for 3D molecular design that uses unbalanced optimal transport to dynamically adapt molecular size, improving property steering and enabling out-of-distribution generation.

Malte Franke, Stefan P. Schmid, Žarko Ivković, Kjell Jorner and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

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NeurIPS 2026Massachusetts Institute of Techn The University of Warwick NVIDIA Molecules & drug discovery

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

SKMD introduces symmetry-aware interacting-particle dynamics for active MLIP learning that preserves Boltzmann sampling, yielding faster convergence with fewer training iterations.

Joanna Zou, Fraser Birks, Dallas Foster, Youssef Marzouk

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 4/5

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NeurIPS 2026U Notre Dame Molecules & drug discovery

Controllable Molecular Generative Foundation Models

CoMole unifies molecular graph generation via motif-aware diffusion and reinforcement learning, achieving top controllability across nine targets with up to 48.2% lower MAE and over 0.94 validity.

Yihan Zhu, Yuhan Liu, Weijiang Li, Tengfei Luo and 1 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 5/5

medium 9/10

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NeurIPS 2026Technical University of Denmark U Oslo JURA Bio, Inc.U Massachusetts Boston Molecules & drug discovery

Lifting Biomolecular Data Acquisition

Neural compressed sensing extends to function space to co-design wet-lab experiments with learning algorithms, achieving orders-of-magnitude higher information density by measuring multiple molecules simultaneously and deconvolving activity during training for antibodies and cell therapies.

Eli N. Weinstein, Andrei Slabodkin, Mattia G Gollub, Kerry Dobbs and 4 more

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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lenient 5/5

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NeurIPS 2026TU Delft Erasmus MC Delft University of Technology Molecules & drug discovery

CompleteRXN: Toward Completing Open Chemical Reaction Databases

CompleteRXN introduces a benchmark for completing incomplete chemical reaction databases, showing models reach high accuracy on benchmark splits but degrade substantially on uncurated real-world data.

Gabriel Vogel, Minouk Noordsij, Evgeny A Pidko, Jana M. Weber

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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lenient 5/5

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NeurIPS 2026Cardiff Molecules & drug discovery

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials

A benchmark of four compositional generalisation tasks reveals state-of-the-art machine learning interatomic potentials fail to generalise to unseen molecules, with out-of-distribution errors often ten times higher than in-distribution errors.

Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni, Steven Schockaert

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

medium 6/10

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NeurIPS 2026Imperial College London Lancaster University / Universit Lancaster Molecules & drug discovery

Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights

AB-SID-iVAR actively learns Gaussian process targets under unknown self-induced Boltzmann weights, achieving vanishing terminal prediction error without partition function estimation.

Jixiang Qing, Henry Moss, Matthias Sachs

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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lenient 3/5

medium 3/10

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NeurIPS 2026Helmholtz Munich Technische Universität München Helmholtz Zentrum München Technical University of Munich Helmholtz Munich GmbH Molecules & drug discovery

Sample Efficient Generative Molecular Optimization with Joint Self-Improvement

Joint Self-Improvement uses a joint generative-predictive model and self-improving sampling to reduce distribution shift and efficiently generate optimized molecules under limited evaluation budgets.

Serra Korkmaz, Adam Izdebski, Jonathan Pirnay, Rasmus Møller-Larsen and 5 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

medium 4/10

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NeurIPS 2026Max Planck Institute of Biochemi Microsoft Research Molecules & drug discovery

Monroe: A Molecular Foundation Model for In-context Probabilistic Inference

Monroe is a molecular foundation model pre-trained on 81 million molecules that uses in-context TabPFN prediction to achieve state-of-the-art bioassay activity prediction, especially on activity cliffs.

Blazej Banaszewski, Andrew Fitzgibbon

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

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NeurIPS 2026Insilico Medicine Insilico Medicine AI Limited Yale Insilico Medicine Canada Insilico Medicine, Inc.Molecules & drug discovery

URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment

URSA benchmarks retrosynthesis via chemistry-aware formal and plausibility metrics, finding specialized models outperform LLMs at reliable synthesis planning.

Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Anton Morgunov and 6 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face

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lenient 5/5

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NeurIPS 2026The Ohio State Yale Texas State Ohio State University, Columbus Molecules & drug discovery

From Retrieval to Reasoning: Agentic Mechanism Prediction from Cell Painting Profiles

PhenoAIR reformulates Cell Painting mechanism prediction as calibrated evidence reasoning via multi-agent evaluation of noisy retrieved neighbors, outperforming matching and LLM baselines across open-world settings.

Jiayuan Chen, Botao Yu, Tianyu Liu, Thai-Hoang Pham and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

medium 8/10

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NeurIPS 2026Shanghai Academy of Artificial I Xiamen SUN YAT-SEN UNIVERSITY nanjing university Fudan Molecules & drug discovery

Equivariant Spherical Transformer for Efficient Molecular Modeling

EST applies Transformers in Fourier spatial domains to boost equivariant GNN expressiveness, achieving state-of-the-art results on OC20 and QM9.

Junyi An, Xinyu Lu, Yun-Fei Shi, Peijia Lin and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 3/5

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NeurIPS 2026CMLA - ENS Paris Saclay, ENS Par Memorial Sloan Kettering Cancer Molecules & drug discovery

ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening

ScreenShot is a hierarchical transformer pretrained on drug screening datasets that predicts combination therapy responses via in-context learning from limited observations without molecular profiling or fine-tuning, outperforming baselines and enabling efficient active screening.

Antoine De mathelin, Christopher Tosh, Wesley Tansey

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 5/5

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NeurIPS 2026California Institute of Technolo MIT ETH Zurich New York Genesis Therapeutics Molecules & drug discovery

On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization

Online discrete diffusion adaptation for molecular optimization finds acquisition, reward shaping, and debiasing complementarily boost reward, with replay and validity control stabilizing exploration to outperform offline and search baselines.

Trevor Chen, Ariel Dai, Jason Yang, Riccardo De Santi and 7 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 5/5

medium 6/10

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NeurIPS 2026KAIST Korea Advanced Institute of Scie Korea Advanced Institute of Scie Korea Advanced Institute of Scie Molecules & drug discovery

A Systematic Evaluation of Co-folding Model Representations for Small-Molecule Learning

Boltz2's protein-ligand co-folding representations match or outperform standalone models on ADMET, generative modeling, and ligand optimization tasks while complementing conventional molecular supervision.

Hyosoon Jang, Hyunjin Seo, Honghui Kim, Taewon Kim and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 13 of 20 reviewers recommend it

lenient 5/5

medium 7/10

strict 1/5