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Open problems in Machine learned interatomic

Reliable geometric reasoning from protein structures1 paper raises it · 2 address it
Frontier models excel at metadata lookup but struggle with numeric geometry tasks, which are critical for structural biology.
Effective integration of biological foundation model inputs1 paper raises it · 1 addresses it
Biological reasoning models often ignore foundation model inputs, relying instead on text, which limits their accuracy and interpretability.
- When Do Biological Reasoning Models Use Their Biological Inputs?raises
- Does Learning Protein Folding Generalize to Broader Reasoning?addresses
Scalable and robust protein sequence and structure generation1 paper raises it · 2 address it
Autoregressive models for protein generation are weak, and current representations lack contextual semantics or efficient discrete encodings.
- Learning Latent Protein Languages for Autoregressive Generationraises · addresses
- recAMPLIFY: Large-Scale Pretraining of Recursive Protein Language Modelsaddresses
Durable and selective pretraining data filtering1 paper raises it · 1 addresses it
Filtering pretraining data can degrade performance on specific domains, but fine-tuning can easily undo these effects, limiting its long-term utility.
- The Effectiveness and Durability of Pretraining Data Filtering in Protein Language Modelsraises · addresses
Stable evaluation metrics for gene perturbation models1 paper raises it · 0 address it
Heterogeneous metrics and DE gene subsets lead to unstable rankings, undermining fair comparisons across methods.
- A Pitfall in the Evaluation of Gene Perturbation Modelsraises

Named by an AI from these papers' abstracts.

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Topics in this field

molecular optimization · molecular design · molecular generation100 papers22 this week
protein language models · protein language · protein structure103 papers14 this week
machine learned interatomic · crystal structure prediction · learned interatomic potentials100 papers11 this week

Topics in this field

Active contributors · 90 days

Ke LiREFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge3
Tianmeng HuREFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge3
Yuqiang LiDrugHarness: An Evolvable Agent Harness for Computational Drug Discovery3
Yatao BianMolLean: A Process-Verifiable Language for Chemical Reasoning3
Frazier N. BakerMFG: Multi-turn Feedback Guided Molecular Optimization3
Xia NingMFG: Multi-turn Feedback Guided Molecular Optimization3

Active contributors

Activity · 12 weeks

Coming with more sources: repos

Activity

Open problems in Machine learned interatomic

Reliable geometric reasoning from protein structures1 paper raises it · 2 address it
Frontier models excel at metadata lookup but struggle with numeric geometry tasks, which are critical for structural biology.
Effective integration of biological foundation model inputs1 paper raises it · 1 addresses it
Biological reasoning models often ignore foundation model inputs, relying instead on text, which limits their accuracy and interpretability.
- When Do Biological Reasoning Models Use Their Biological Inputs?raises
- Does Learning Protein Folding Generalize to Broader Reasoning?addresses
Scalable and robust protein sequence and structure generation1 paper raises it · 2 address it
Autoregressive models for protein generation are weak, and current representations lack contextual semantics or efficient discrete encodings.
- Learning Latent Protein Languages for Autoregressive Generationraises · addresses
- recAMPLIFY: Large-Scale Pretraining of Recursive Protein Language Modelsaddresses
Durable and selective pretraining data filtering1 paper raises it · 1 addresses it
Filtering pretraining data can degrade performance on specific domains, but fine-tuning can easily undo these effects, limiting its long-term utility.
- The Effectiveness and Durability of Pretraining Data Filtering in Protein Language Modelsraises · addresses
Stable evaluation metrics for gene perturbation models1 paper raises it · 0 address it
Heterogeneous metrics and DE gene subsets lead to unstable rankings, undermining fair comparisons across methods.
- A Pitfall in the Evaluation of Gene Perturbation Modelsraises

Named by an AI from these papers' abstracts.

Activity · 12 weeks

Coming with more sources: repos

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arXiv cs.LGOct 17
Generative Retrieval for Reverse Target Fishing with a Proteome-Scale Benchmark
Zhichao Deng, Haichuan Tan, Xin Hong, Huaizhi Wang, Tao Huang +2
Reverse target fishing identifies protein targets for a query molecule and is central to drug discovery and repurposing. Discriminative approaches adapted from virtual screening face sparse, long-tailed associations and missing-not-at-random measurements: treating unrecorded pairs as negatives can suppress genuine targets. We reformulate the task as generative retrieval and introduce GenFish. Residual vector quantization converts frozen protein language model features into shared semantic identifiers. Given a molecular fingerprint, GenFish autoregressively models codes conditioned on the query and preceding code, ranking proteins by identifier likelihood without explicitly labeling unrecorded pairs as negatives. We introduce ProTargetBench, the first proteome-scale benchmark for reverse target fishing to jointly support measured-panel discrimination and full-catalogue recovery of experimentally recorded positives. Covering 20,431 UniProt-reviewed human proteins, it integrates curated bioactivity evidence and distinguishes experimental inactivity from unmeasured associations. On 198 held-out molecules, GenFish achieves higher mean performance across all three full-catalogue…
PDF arXiv
arXiv cs.AIOct 16
CME-SpectrumBench: Can LLMs Analyze Condensed Matter Spectral Data?
Jin Gene Wong, Anjney Midha, Joseph Mathew Tennyson, Wei-Lin Chiang, Ion Stoica +1
Large Language Models (LLMs) are presently well assessed by expert-level mathematics and physics benchmarks which focus on theoretical, symbolic reasoning. However, we lack experimental benchmarks that evaluate data analysis in frontier research problems, a capability that future domain-specific foundation models must have. To this end, we present CME-SpectrumBench, an expert-designed benchmark consisting of 190 questions that measures how reliably frontier LLMs analyze research-level spectral scientific data in condensed matter experiments (CME). Using simulated CME spectral data, we prompt models to identify structures from 2D intensity arrays in the face of noise and limited instrumental resolution, a test of dense context reasoning. Models are scored by how accurately their numerical answers approach the ground truth. Under direct query, scores generally pale in comparison to handwritten code due to clear failure modes: models can find simple features but struggle to trace patterns or compute derived quantities, and performance typically degrades with increased data resolution and noise/convolution levels. LLM performance becomes comparable to handwritten code by allowing…
PDF arXiv
arXiv cs.LGOct 16
An important Bottleneck in LLM Optimizers: Principle-Level Local Optima
Nian Ran, Tianhe Jia, Zhengyang Mu, Yue Wang, Sebastian Brulin +4
Feedback-driven LLM optimizers are increasingly used for evolutionary search, Bayesian optimization, heuristic design, and scientific discovery. Yet rapid initial gains often give way to prolonged plateaus despite substantial remaining evaluation budgets. We investigate \emph{principle-level search collapse}, where an optimizer's search becomes confined to a narrow set of design principles, even though it may continue to generate distinct candidates and achieve small improvements. We assemble a diagnostic benchmark spanning molecular optimization, network sentinel-node selection, and GPU-kernel optimization, and introduce a decision-tree protocol combining principle narrowing, prolonged performance plateaus, and gaps to attainable reference solutions. Across 161 assessable runs on the three problem domains, 78.3\% receive support for collapse, including 55.3\% with strong support. Candidate-level variation can nevertheless conceal restricted search at the principle level. We then test \emph{diversity injection}, a common response to search stagnation, such as higher t, multiple LLMs, and web search, and show that it provides limited and inconsistent benefits: among 65 molecular…
PDF arXiv
arXiv q-bio.GNOct 16
Reducing double-dipping in single-cell topic models with three-way count splitting
Zheng Qu
Topic models learn gene programs from single-cell RNA sequencing (scRNA-seq) data, and the same counts are then used to test which genes and donors are associated with those programs. Count splitting is the standard remedy for this reuse, but it has not been tested when an encoder reads each cell's counts. On a 104,400-cell lupus cohort with 300 permuted null genes per run, naive tests rejected 36% of null gene-topic pairs at a nominal 5% level, and the usual two-way split rejected 38%, because the encoder sees the tested gene's own held-out count. Encoding topic usage on a separate third of the counts, with beta-binomial thinning, lowered this to 7.7% (6.7% for genes outside the model); for two other models, LDVAE and scETM, the rate was 7.0-7.3% (naive 51-59%). A label-free interferon program shifts in held-out counts in the lupus cohort and in a pre-specified COVID-19 cohort, and donor-level topic usage predicts disease (AUROC 0.96) beyond cell-type proportions (0.86 and 0.58 in the two cohorts). Calibration and prediction did not depend on the model. We do not claim better clustering, prediction or topic stability. Code, figures and result tables are public (MIT license; DOI…
PDF arXiv
arXiv cs.LGOct 16
On the Selectivity of Generative Models in Structure-Based Drug Design
Ella Miray Rajaonson, Jungyoon Lee, William Chau, Alan Aspuru-Guzik, Benjamin Sanchez-Lengeling +3
Designing small-molecule drugs that selectively bind to a target protein while avoiding unintended interactions with other proteins is essential for reducing side effects that contribute substantially to clinical attrition. Although structure-based drug design (SBDD) has advanced rapidly with deep learning, most generative models still optimize binding to a single target and disregard selectivity due to limited training data and unknown off-target interactions. Motivated by this gap, we introduce SELECTBENCH, a comprehensive benchmark assessing the selectivity of generative SBDD models. SELECTBENCH provides three curated evaluation tasks reflecting different real-world drug discovery scenarios: literature-based case studies, safety screening panels used in pharmaceutical development, and large-scale structure-based evaluation sets. We further introduce a suite of metrics to quantify selectivity and conduct a systematic evaluation of nine representative SBDD models, including methods explicitly trained for selectivity and widely-used inference-time guidance techniques. Our results show that even selectivity-trained models achieve only modest off-target discrimination and…
PDF arXiv
arXiv cs.LGOct 16
CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning
Tianmeng Hu, Biao Luo, Ke Li
Designing RNA sequences that reliably fold into specific secondary structures is essential for understanding their biological functions but remains a challenging computational problem. We propose CocoRNA, a cooperative multi-agent reinforcement learning framework for RNA inverse design. CocoRNA simplifies the design task by decomposing it into smaller sub-problems, each solved collaboratively by multiple agents. This approach reduces the complexity of the problem and improves the exploration of design policies. During training, a centralized critic uses global structural information to guide the agents, enabling them to jointly optimize their design strategies. As a result, CocoRNA learns high-quality RNA design policies that generalize effectively to unseen structures without additional training. Experiments on the Rfam dataset demonstrate that CocoRNA substantially outperforms state-of-the-art methods in both success rate and design speed. Further experiments on other biological sequence design tasks highlight the effectiveness and broad potential of CocoRNA for complex design tasks.
PDF arXiv

Papers

Topics in this field

molecular optimization · molecular design · molecular generation100 papers22 this week
protein language models · protein language · protein structure103 papers14 this week
machine learned interatomic · crystal structure prediction · learned interatomic potentials100 papers11 this week

Active contributors · 90 days

Ke LiREFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge3
Tianmeng HuREFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge3
Yuqiang LiDrugHarness: An Evolvable Agent Harness for Computational Drug Discovery3
Yatao BianMolLean: A Process-Verifiable Language for Chemical Reasoning3
Frazier N. BakerMFG: Multi-turn Feedback Guided Molecular Optimization3
Xia NingMFG: Multi-turn Feedback Guided Molecular Optimization3

Fields · AI · updated 36m ago

Machine learned interatomic

Found by the topic model: machine learned interatomic, protein language models, molecular optimization.

47papers this week−39.0% vs last week

All papers · 303 With code · 4

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284 papers this month

Recent papers

All in Research →

arXiv cs.LGOct 17
Generative Retrieval for Reverse Target Fishing with a Proteome-Scale Benchmark
Zhichao Deng, Haichuan Tan, Xin Hong, Huaizhi Wang, Tao Huang +2
Reverse target fishing identifies protein targets for a query molecule and is central to drug discovery and repurposing. Discriminative approaches adapted from virtual screening face sparse, long-tailed associations and missing-not-at-random measurements: treating unrecorded pairs as negatives can suppress genuine targets. We reformulate the task as generative retrieval and introduce GenFish. Residual vector quantization converts frozen protein language model features into shared semantic identifiers. Given a molecular fingerprint, GenFish autoregressively models codes conditioned on the query and preceding code, ranking proteins by identifier likelihood without explicitly labeling unrecorded pairs as negatives. We introduce ProTargetBench, the first proteome-scale benchmark for reverse target fishing to jointly support measured-panel discrimination and full-catalogue recovery of experimentally recorded positives. Covering 20,431 UniProt-reviewed human proteins, it integrates curated bioactivity evidence and distinguishes experimental inactivity from unmeasured associations. On 198 held-out molecules, GenFish achieves higher mean performance across all three full-catalogue…
PDF arXiv
arXiv cs.AIOct 16
CME-SpectrumBench: Can LLMs Analyze Condensed Matter Spectral Data?
Jin Gene Wong, Anjney Midha, Joseph Mathew Tennyson, Wei-Lin Chiang, Ion Stoica +1
Large Language Models (LLMs) are presently well assessed by expert-level mathematics and physics benchmarks which focus on theoretical, symbolic reasoning. However, we lack experimental benchmarks that evaluate data analysis in frontier research problems, a capability that future domain-specific foundation models must have. To this end, we present CME-SpectrumBench, an expert-designed benchmark consisting of 190 questions that measures how reliably frontier LLMs analyze research-level spectral scientific data in condensed matter experiments (CME). Using simulated CME spectral data, we prompt models to identify structures from 2D intensity arrays in the face of noise and limited instrumental resolution, a test of dense context reasoning. Models are scored by how accurately their numerical answers approach the ground truth. Under direct query, scores generally pale in comparison to handwritten code due to clear failure modes: models can find simple features but struggle to trace patterns or compute derived quantities, and performance typically degrades with increased data resolution and noise/convolution levels. LLM performance becomes comparable to handwritten code by allowing…
PDF arXiv
arXiv cs.LGOct 16
An important Bottleneck in LLM Optimizers: Principle-Level Local Optima
Nian Ran, Tianhe Jia, Zhengyang Mu, Yue Wang, Sebastian Brulin +4
Feedback-driven LLM optimizers are increasingly used for evolutionary search, Bayesian optimization, heuristic design, and scientific discovery. Yet rapid initial gains often give way to prolonged plateaus despite substantial remaining evaluation budgets. We investigate \emph{principle-level search collapse}, where an optimizer's search becomes confined to a narrow set of design principles, even though it may continue to generate distinct candidates and achieve small improvements. We assemble a diagnostic benchmark spanning molecular optimization, network sentinel-node selection, and GPU-kernel optimization, and introduce a decision-tree protocol combining principle narrowing, prolonged performance plateaus, and gaps to attainable reference solutions. Across 161 assessable runs on the three problem domains, 78.3\% receive support for collapse, including 55.3\% with strong support. Candidate-level variation can nevertheless conceal restricted search at the principle level. We then test \emph{diversity injection}, a common response to search stagnation, such as higher t, multiple LLMs, and web search, and show that it provides limited and inconsistent benefits: among 65 molecular…
PDF arXiv
arXiv q-bio.GNOct 16
Reducing double-dipping in single-cell topic models with three-way count splitting
Zheng Qu
Topic models learn gene programs from single-cell RNA sequencing (scRNA-seq) data, and the same counts are then used to test which genes and donors are associated with those programs. Count splitting is the standard remedy for this reuse, but it has not been tested when an encoder reads each cell's counts. On a 104,400-cell lupus cohort with 300 permuted null genes per run, naive tests rejected 36% of null gene-topic pairs at a nominal 5% level, and the usual two-way split rejected 38%, because the encoder sees the tested gene's own held-out count. Encoding topic usage on a separate third of the counts, with beta-binomial thinning, lowered this to 7.7% (6.7% for genes outside the model); for two other models, LDVAE and scETM, the rate was 7.0-7.3% (naive 51-59%). A label-free interferon program shifts in held-out counts in the lupus cohort and in a pre-specified COVID-19 cohort, and donor-level topic usage predicts disease (AUROC 0.96) beyond cell-type proportions (0.86 and 0.58 in the two cohorts). Calibration and prediction did not depend on the model. We do not claim better clustering, prediction or topic stability. Code, figures and result tables are public (MIT license; DOI…
PDF arXiv
arXiv cs.LGOct 16
On the Selectivity of Generative Models in Structure-Based Drug Design
Ella Miray Rajaonson, Jungyoon Lee, William Chau, Alan Aspuru-Guzik, Benjamin Sanchez-Lengeling +3
Designing small-molecule drugs that selectively bind to a target protein while avoiding unintended interactions with other proteins is essential for reducing side effects that contribute substantially to clinical attrition. Although structure-based drug design (SBDD) has advanced rapidly with deep learning, most generative models still optimize binding to a single target and disregard selectivity due to limited training data and unknown off-target interactions. Motivated by this gap, we introduce SELECTBENCH, a comprehensive benchmark assessing the selectivity of generative SBDD models. SELECTBENCH provides three curated evaluation tasks reflecting different real-world drug discovery scenarios: literature-based case studies, safety screening panels used in pharmaceutical development, and large-scale structure-based evaluation sets. We further introduce a suite of metrics to quantify selectivity and conduct a systematic evaluation of nine representative SBDD models, including methods explicitly trained for selectivity and widely-used inference-time guidance techniques. Our results show that even selectivity-trained models achieve only modest off-target discrimination and…
PDF arXiv
arXiv cs.LGOct 16
CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning
Tianmeng Hu, Biao Luo, Ke Li
Designing RNA sequences that reliably fold into specific secondary structures is essential for understanding their biological functions but remains a challenging computational problem. We propose CocoRNA, a cooperative multi-agent reinforcement learning framework for RNA inverse design. CocoRNA simplifies the design task by decomposing it into smaller sub-problems, each solved collaboratively by multiple agents. This approach reduces the complexity of the problem and improves the exploration of design policies. During training, a centralized critic uses global structural information to guide the agents, enabling them to jointly optimize their design strategies. As a result, CocoRNA learns high-quality RNA design policies that generalize effectively to unseen structures without additional training. Experiments on the Rfam dataset demonstrate that CocoRNA substantially outperforms state-of-the-art methods in both success rate and design speed. Further experiments on other biological sequence design tasks highlight the effectiveness and broad potential of CocoRNA for complex design tasks.
PDF arXiv