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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 papers19 this week
protein language models · protein language · protein structure107 papers16 this week
machine learned interatomic · crystal structure prediction · learned interatomic potentials101 papers10 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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arXiv cond-mat.mtrl-sciCodeOct 6▲ 16
A self-learning scientific agent for X-ray diffraction
Bin Cao, Huichi Zhou, Runyu Yang, Jingsong Li, Shuchen Sun +5
A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physical evidence. Here we introduce Gan Jiang, a self-learning agent for powder X-ray diffraction built on a diffraction-analysis ecosystem we developed: XMatcher, XQueryer, XDecomposer and WPEM. Together, these engines span phase identification, multiphase decomposition and physics-constrained whole-pattern modelling. Gan Jiang converts analytical experience into executable skills by diagnosing failures, revising skill instructions and code, and validating revisions before reuse, without retraining the language model or changing the underlying physical models. Skills selected using development data and frozen before held-out evaluation achieve higher refinement scores than the original expert-designed skills across FullProf, GSAS-II and PyWPEM. The agent resolves strongly overlapping reflections, quantifies a five-phase ancient Egyptian cosmetic, tracks lattice evolution in an operating battery and compares atomic configurations in a disordered oxide catalyst. On DeltaXRDbench, it leads the evaluated methods in single- and multiphase identification across simulated and…
PDF arXiv Code
arXiv q-bio.QMOct 6
Linear Fitness Subspace in Protein Language Models Enables Sample-Efficient Directed Evolution
SiYuan Ma, Canran Xiao, Zikai Xiao, Albert Gao, Liang He +3
Model-guided directed evolution seeks to identify high-fitness protein variants under limited oracle budgets. Protein language models (PLMs) provide rich representations for this task, but task-agnostic zero-shot scores can be misaligned with a target assay, while supervised search in high-dimensional embedding spaces can make surrogate modeling and uncertainty estimation sample-inefficient. We propose the Linear Fitness Subspace (LFS) hypothesis: within mutation-induced residue-level representation changes, a compact, assay-specific set of directions makes fitness variation linearly accessible from few labeled variants. This is a local, supervision-recoverable statement rather than a claim that protein fitness landscapes or global PLM geometry are universally linear. Building on this observation, we introduce Subspace-Guided Evolutionary Search (SGES), which estimates an LFS from a small initial sample and performs surrogate modeling, uncertainty estimation, and acquisition in the learned subspace. Across 10 core ProteinGym assays, 87 extended static-validation assays, and an 18-assay budgeted-search evaluation, SGES improves fitness prediction and search efficiency over…
PDF arXiv
arXiv cs.AIOct 6
Navigating Route Latent Space for Synthesizable Molecular Design
Tao Li, Tuan Vinh, Monika Raj, Yuan Fang, Zhichun Guo +1
Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, limiting their real-world utility. Prior synthesizability-aware methods either project generated molecules back to synthesizable analogs that deviate from the intended target, or optimize directly in discrete synthesis spaces that lack a continuous landscape for efficient search. We argue that this limitation mainly comes from the search space rather than the optimizer. To address this, we propose RouteFlow, a framework that reformulates synthesizable molecular design as a search over a continuous route latent space, where each latent maps back to a complete synthesis route and synthesizability is inherently preserved. To navigate this space, we adopt reward-guided flow matching as an efficient sampler that steers toward high-property regions. Since reward optimization may push latents off the manifold of real synthesis routes, where decoding becomes unreliable, we further introduce a cycle-consistency mechanism to stabilize fine-tuning. Across 16 optimization tasks from Therapeutic Data Commons, RouteFlow achieves the best sample…
PDF arXiv
arXiv cs.LGOct 5
CausalBind: Causal Modeling and Learning for Protein-Molecule Virtual Screening
Loka Li, Jin Tian, Kun Zhang
Protein-molecule virtual screening is increasingly cast as a problem of representation learning in a shared embedding space. Existing methods rely on dense holistic alignment, entangling invariant binding determinants with nuisance correlations and limiting transfer to new targets. It has been noted that binding in protein-molecule systems involves sparse cross-modality interactions: binding is governed by a small contact interface and a few decisive local interactions (e.g., hydrogen bonds, hydrophobic contacts, and salt bridges) rather than the global structures of the protein and molecule. We hypothesize that uncovering and leveraging sparse interaction patterns is critical for generalization beyond the training data, as these patterns are reusable and expected to improve performance across different scenarios. In this paper, we aim to identify and leverage sparse interaction patterns, and verify our hypothesis. Since the training data contain only observed binding pairs, we formalize this prior via a V-structure causal model under Heckman-style selection, and establish three theoretical results: (i) the latent concepts of interacting proteins and molecules are not…
PDF arXiv
arXiv cs.AIOct 5
FREA: A Multi-Source Expert Benchmark for Reaction Feasibility Verification
Botao Yu, Bo Zhou, Daniel Adu-Ampratwum, Frazier N. Baker, Ziru Chen +5
As generative models and AI agents propose chemical reactions at a scale beyond expert review, feasibility verifiers decide which proposals enter synthesis planning. But do their decisions agree with chemists across different kinds of candidates? We introduce FREA, a benchmark of 751 reactions labeled by expert chemists under an explicit feasibility criterion, drawn from retrosynthesis model proposals, zero-yield experimental records, edits by large language models (LLMs), and five negative candidate generation methods. Our evaluation finds that no verifier leads across all sources: LLMs given only the criterion are competitive with dedicated verifiers, while forward models perform best on retrosynthesis proposals but reject most feasible edits of recorded reactions at the evaluated operating points. Looking beyond aggregate scores, both forward models perform below chance when separating infeasible alternative disconnections from feasible generated candidates. To study whether negative supervision addresses these weaknesses, we also release a corpus of over 14 million recorded reactions and generated negative candidates. In matched training comparisons, adding a mixture of…
PDF arXiv
arXiv cs.LGOct 5
ARO: Aligned Representation learning for multi-Omics data
Amogh Singh, Yash Shah, Chiara D'Ercoli, Arash Mehrjou, Patrick Schwab +2
The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant barriers to comprehensive multi-omic profiling, essential for capturing and reasoning over molecules, cells, tissues, and organisms. This work proposes a model that learns meaningful representations from multi-omics cancer data supporting the reconstruction of missing and unpaired modalities. Contrary to increasingly complex, larger models, e.g. Foundation Models (FMs), ARO prioritizes practical applicability in limited or incomplete data settings. ARO optimally reconstructs missing modalities (MSE of $0.15$ on the validation and test data in the Unmasked settings), with its learned latent embeddings enabling a downstream cancer classification task. Our findings indicate that analyzing diverse molecular layers as a single integrated system offers a reliable and cost-efficient approach, reducing dependence on large-scale experimental testing, while still supporting multi-omic exploration in limited data settings.
PDF arXiv

Papers

Topics in this field

molecular optimization · molecular design · molecular generation100 papers19 this week
protein language models · protein language · protein structure107 papers16 this week
machine learned interatomic · crystal structure prediction · learned interatomic potentials101 papers10 this week

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

Recent papers

All in Research →

arXiv cond-mat.mtrl-sciCodeOct 6▲ 16
A self-learning scientific agent for X-ray diffraction
Bin Cao, Huichi Zhou, Runyu Yang, Jingsong Li, Shuchen Sun +5
A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physical evidence. Here we introduce Gan Jiang, a self-learning agent for powder X-ray diffraction built on a diffraction-analysis ecosystem we developed: XMatcher, XQueryer, XDecomposer and WPEM. Together, these engines span phase identification, multiphase decomposition and physics-constrained whole-pattern modelling. Gan Jiang converts analytical experience into executable skills by diagnosing failures, revising skill instructions and code, and validating revisions before reuse, without retraining the language model or changing the underlying physical models. Skills selected using development data and frozen before held-out evaluation achieve higher refinement scores than the original expert-designed skills across FullProf, GSAS-II and PyWPEM. The agent resolves strongly overlapping reflections, quantifies a five-phase ancient Egyptian cosmetic, tracks lattice evolution in an operating battery and compares atomic configurations in a disordered oxide catalyst. On DeltaXRDbench, it leads the evaluated methods in single- and multiphase identification across simulated and…
PDF arXiv Code
arXiv q-bio.QMOct 6
Linear Fitness Subspace in Protein Language Models Enables Sample-Efficient Directed Evolution
SiYuan Ma, Canran Xiao, Zikai Xiao, Albert Gao, Liang He +3
Model-guided directed evolution seeks to identify high-fitness protein variants under limited oracle budgets. Protein language models (PLMs) provide rich representations for this task, but task-agnostic zero-shot scores can be misaligned with a target assay, while supervised search in high-dimensional embedding spaces can make surrogate modeling and uncertainty estimation sample-inefficient. We propose the Linear Fitness Subspace (LFS) hypothesis: within mutation-induced residue-level representation changes, a compact, assay-specific set of directions makes fitness variation linearly accessible from few labeled variants. This is a local, supervision-recoverable statement rather than a claim that protein fitness landscapes or global PLM geometry are universally linear. Building on this observation, we introduce Subspace-Guided Evolutionary Search (SGES), which estimates an LFS from a small initial sample and performs surrogate modeling, uncertainty estimation, and acquisition in the learned subspace. Across 10 core ProteinGym assays, 87 extended static-validation assays, and an 18-assay budgeted-search evaluation, SGES improves fitness prediction and search efficiency over…
PDF arXiv
arXiv cs.AIOct 6
Navigating Route Latent Space for Synthesizable Molecular Design
Tao Li, Tuan Vinh, Monika Raj, Yuan Fang, Zhichun Guo +1
Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, limiting their real-world utility. Prior synthesizability-aware methods either project generated molecules back to synthesizable analogs that deviate from the intended target, or optimize directly in discrete synthesis spaces that lack a continuous landscape for efficient search. We argue that this limitation mainly comes from the search space rather than the optimizer. To address this, we propose RouteFlow, a framework that reformulates synthesizable molecular design as a search over a continuous route latent space, where each latent maps back to a complete synthesis route and synthesizability is inherently preserved. To navigate this space, we adopt reward-guided flow matching as an efficient sampler that steers toward high-property regions. Since reward optimization may push latents off the manifold of real synthesis routes, where decoding becomes unreliable, we further introduce a cycle-consistency mechanism to stabilize fine-tuning. Across 16 optimization tasks from Therapeutic Data Commons, RouteFlow achieves the best sample…
PDF arXiv
arXiv cs.LGOct 5
CausalBind: Causal Modeling and Learning for Protein-Molecule Virtual Screening
Loka Li, Jin Tian, Kun Zhang
Protein-molecule virtual screening is increasingly cast as a problem of representation learning in a shared embedding space. Existing methods rely on dense holistic alignment, entangling invariant binding determinants with nuisance correlations and limiting transfer to new targets. It has been noted that binding in protein-molecule systems involves sparse cross-modality interactions: binding is governed by a small contact interface and a few decisive local interactions (e.g., hydrogen bonds, hydrophobic contacts, and salt bridges) rather than the global structures of the protein and molecule. We hypothesize that uncovering and leveraging sparse interaction patterns is critical for generalization beyond the training data, as these patterns are reusable and expected to improve performance across different scenarios. In this paper, we aim to identify and leverage sparse interaction patterns, and verify our hypothesis. Since the training data contain only observed binding pairs, we formalize this prior via a V-structure causal model under Heckman-style selection, and establish three theoretical results: (i) the latent concepts of interacting proteins and molecules are not…
PDF arXiv
arXiv cs.AIOct 5
FREA: A Multi-Source Expert Benchmark for Reaction Feasibility Verification
Botao Yu, Bo Zhou, Daniel Adu-Ampratwum, Frazier N. Baker, Ziru Chen +5
As generative models and AI agents propose chemical reactions at a scale beyond expert review, feasibility verifiers decide which proposals enter synthesis planning. But do their decisions agree with chemists across different kinds of candidates? We introduce FREA, a benchmark of 751 reactions labeled by expert chemists under an explicit feasibility criterion, drawn from retrosynthesis model proposals, zero-yield experimental records, edits by large language models (LLMs), and five negative candidate generation methods. Our evaluation finds that no verifier leads across all sources: LLMs given only the criterion are competitive with dedicated verifiers, while forward models perform best on retrosynthesis proposals but reject most feasible edits of recorded reactions at the evaluated operating points. Looking beyond aggregate scores, both forward models perform below chance when separating infeasible alternative disconnections from feasible generated candidates. To study whether negative supervision addresses these weaknesses, we also release a corpus of over 14 million recorded reactions and generated negative candidates. In matched training comparisons, adding a mixture of…
PDF arXiv
arXiv cs.LGOct 5
ARO: Aligned Representation learning for multi-Omics data
Amogh Singh, Yash Shah, Chiara D'Ercoli, Arash Mehrjou, Patrick Schwab +2
The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant barriers to comprehensive multi-omic profiling, essential for capturing and reasoning over molecules, cells, tissues, and organisms. This work proposes a model that learns meaningful representations from multi-omics cancer data supporting the reconstruction of missing and unpaired modalities. Contrary to increasingly complex, larger models, e.g. Foundation Models (FMs), ARO prioritizes practical applicability in limited or incomplete data settings. ARO optimally reconstructs missing modalities (MSE of $0.15$ on the validation and test data in the Unmasked settings), with its learned latent embeddings enabling a downstream cancer classification task. Our findings indicate that analyzing diverse molecular layers as a single integrated system offers a reliable and cost-efficient approach, reducing dependence on large-scale experimental testing, while still supporting multi-omic exploration in limited data settings.
PDF arXiv

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.

Fields · AI · updated 33m ago

Machine learned interatomic

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

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