Papers

Field

Top tools here

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.

Open problems

Recently funded

Topics in this field

molecular optimization · molecular design · molecular generation100 papers22 this week
protein language models · protein language · protein structure106 papers17 this week
machine learned interatomic · crystal structure prediction · learned interatomic potentials101 papers12 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Coming with more sources: repos

Activity

Recent papers

All in Research →

arXiv cs.AIOct 16
DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery
Yuxing Chen, Zhisheng Zhang, Cheng Tang, Wei Li, Minghui Zhang +18
Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHarness, an evolvable agent harness coupling composable scientific execution with conditional skill induction. Its environment integrates over 80 scientific operations, biomedical resources and task-specific code, preserving associations among candidates, results and artifacts. From execution trajectories and outcome feedback, DrugHarness induces action hypotheses with observable applicability conditions, recommended actions and supporting evidence. An independent review agent evaluates candidate skills derived from these hypotheses through source verification and, when needed, targeted execution trials. Admitted skills guide subsequent tasks and are revised through feedback. Evaluation on MolBench and SMDD-Bench demonstrates capabilities in molecular screening, optimization and constrained design. Without evolution, DrugHarness exceeds reported MolClaw-CC results by 10.8 percentage points in affinity-comparison accuracy and 55% in mean top-three active hits on MolBench. Retrospective evaluation on skill-development tasks shows that…
PDF arXiv
arXiv cond-mat.dis-nnOct 16
Sampling the XY model with an attention-based neural network
Piotr Białas, Christian D. Morales, Tomasz Stebel, Dawid Zapolski
We present a transformer-based variational autoregressive network for sampling lattice systems with continuous degrees of freedom and apply it to the two-dimensional XY model near the Berezinskii-Kosterlitz-Thouless transition. The architecture is combined with physics-informed ingredients: locality-informed attention and approximate probabilities. The resulting sampler significantly outperforms existing neural samplers. We train the model on lattices up to $96\times96$, verify with a mode-dropping estimator that no parts of the configuration space are missed, and reproduce the energy, magnetization and helicity modulus obtained with the traditional Markov Chain Monte Carlo algorithm.
PDF arXiv
arXiv cs.LGOct 16
Blitz: Accelerating the Diffusion Modules of AlphaFold3 and Boltz-2
Vinay Saji Mathew, Soundar Kumara, Gretta Kellogg, William K. M. Lai
Diffusion-based protein structure prediction models produce all-atom structures in about 200 denoising steps, and that cost has driven a number of few-step samplers. We present Blitz, a few-step distillation framework that uses a geometric noise schedule to distribute steps more evenly across noise scales, then learns those updates through multistep consistency training. Physical steering and target-free candidate selection help reduce the remaining geometric defects at inference. We apply Blitz to AlphaFold 3 and Boltz-2, reducing denoising from 200 steps to eight. Because published few-step results use different benchmarks and scoring procedures, we compare both distilled models with their teachers and other few-step methods under a common evaluation protocol covering structural accuracy, interface quality, ligand placement, and stereochemical validity. Blitz retains teacher-level structural accuracy, with AF3-Blitz outperforming all evaluated few-step baselines and exceeding its teacher on several accuracy metrics.
PDF arXiv
arXiv cond-mat.mtrl-sciOct 16
Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling
Yicheng Chen, Xiaoyun Yu, Hyejeong Song, Xintian Wang, Zihui Li +3
Accurate atomistic simulations of materials under electric fields require long-range electrostatics and electrical-response modeling at length scales beyond the reach of conventional first-principles methods. Yet these capabilities remain difficult to reconcile with the scalability of machine-learning interatomic potentials (MLIPs). Strictly local models enable efficient spatial parallelization but neglect long-range interactions, whereas existing electrostatic extensions introduce global dependencies that hinder distributed computation. Here, we overcome this limitation by analytically separating the long-range electrostatic forces and Born effective charges (BECs) into a global electrostatics calculation and strictly local derivatives of learned latent charges. This formulation preserves the spatial decomposability of equivariant MLIPs while enabling distributed evaluation of energies, forces, stresses, and BECs across different boundary conditions. Implemented as DPA4C-LR on the \texttt{nvalchemi} platform, the framework scales to 5.2 million atoms on four GPUs at approximately twice the computational cost of its short-range backbone. Trained on energies and forces, DPA4C-LR…
PDF arXiv
arXiv cs.AIOct 16
GEMS: Molecular Structure Elucidation as Learned Search over Isomer Graphs
Utku Umur Acikalin, Yingheng Wang, Goncalo J. Gouveia, Tyler J. Schwertfeger, Frank C. Schroeder +1
Identifying small-molecule structures from molecular formulas and tandem mass spectra remains a bottleneck in untargeted metabolomics and natural-product discovery. Even when database retrieval or de novo prediction misses the exact structure, the resulting candidates can provide starting points for exploring nearby structures through a few bond edits. We introduce GEMS (Guided Editing of Molecular Structures), which refines candidates through learned 2-switch bond edits that preserve the molecular formula and each atom's total valence. GEMS uses MIST, a pretrained spectrum-to-fingerprint model, to predict fingerprint probabilities once per spectrum. A neural policy compares each candidate's fingerprint with these predictions, links present features to the atoms and bonds that generate them, and scores all valid edits in one forward pass. The policy is trained on shortest edit paths between structures, so it can be pretrained on PubChem isomers without spectra and then fine-tuned with spectrum-predicted fingerprints. At inference, GEMS searches from multiple initial candidates matching the supplied molecular formula and ranks candidates by fingerprint agreement and multi-start…
PDF arXiv
arXiv cs.CVOct 16
LithoWorld: An Endpoint World Model for Lithography Process-Window Recovery
Botong Zhao, Fang Yu, Xinyuan Chen, Yue Lu
Lithography process drift can move a fabricated pattern outside its process window, producing unqualified scanning electron microscopy (SEM) morphology. We present LithoWorld, an endpoint world model for SEM-guided parameter recovery. Interior representative targets jointly determine qualified reference appearance and correction supervision. A causal predictive branch aligns currentimage features with endpoint representations, while a conditional flow policy generates a two-dimensional correction as an action chunk. The model learns from 173 groups of measured SEMs organized as focus-exposure maps, preserving unmeasured cells as unknown. On 200 test pairs from the 73-group subset, LithoWorld achieves 88.5% qualified-region success and 46.0% interior success, exceeding the strongest comparison method by 2.5 and 6.0 percentage points, respectively. Ablations separate the effects of target selection, endpoint alignment, and output parameterization. Qualitative SEM retrieval illustrates recovery to qualified interiors without requiring exact reference-coordinate matching.
PDF arXiv

Papers

Topics in this field

molecular optimization · molecular design · molecular generation100 papers22 this week
protein language models · protein language · protein structure106 papers17 this week
machine learned interatomic · crystal structure prediction · learned interatomic potentials101 papers12 this week

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 13m ago

Machine learned interatomic

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

51papers this week−33.8% vs last week

All papers · 307 With code · 4

Newest Most relevant

288 papers this month

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.

Recent papers

All in Research →

arXiv cs.AIOct 16
DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery
Yuxing Chen, Zhisheng Zhang, Cheng Tang, Wei Li, Minghui Zhang +18
Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHarness, an evolvable agent harness coupling composable scientific execution with conditional skill induction. Its environment integrates over 80 scientific operations, biomedical resources and task-specific code, preserving associations among candidates, results and artifacts. From execution trajectories and outcome feedback, DrugHarness induces action hypotheses with observable applicability conditions, recommended actions and supporting evidence. An independent review agent evaluates candidate skills derived from these hypotheses through source verification and, when needed, targeted execution trials. Admitted skills guide subsequent tasks and are revised through feedback. Evaluation on MolBench and SMDD-Bench demonstrates capabilities in molecular screening, optimization and constrained design. Without evolution, DrugHarness exceeds reported MolClaw-CC results by 10.8 percentage points in affinity-comparison accuracy and 55% in mean top-three active hits on MolBench. Retrospective evaluation on skill-development tasks shows that…
PDF arXiv
arXiv cond-mat.dis-nnOct 16
Sampling the XY model with an attention-based neural network
Piotr Białas, Christian D. Morales, Tomasz Stebel, Dawid Zapolski
We present a transformer-based variational autoregressive network for sampling lattice systems with continuous degrees of freedom and apply it to the two-dimensional XY model near the Berezinskii-Kosterlitz-Thouless transition. The architecture is combined with physics-informed ingredients: locality-informed attention and approximate probabilities. The resulting sampler significantly outperforms existing neural samplers. We train the model on lattices up to $96\times96$, verify with a mode-dropping estimator that no parts of the configuration space are missed, and reproduce the energy, magnetization and helicity modulus obtained with the traditional Markov Chain Monte Carlo algorithm.
PDF arXiv
arXiv cs.LGOct 16
Blitz: Accelerating the Diffusion Modules of AlphaFold3 and Boltz-2
Vinay Saji Mathew, Soundar Kumara, Gretta Kellogg, William K. M. Lai
Diffusion-based protein structure prediction models produce all-atom structures in about 200 denoising steps, and that cost has driven a number of few-step samplers. We present Blitz, a few-step distillation framework that uses a geometric noise schedule to distribute steps more evenly across noise scales, then learns those updates through multistep consistency training. Physical steering and target-free candidate selection help reduce the remaining geometric defects at inference. We apply Blitz to AlphaFold 3 and Boltz-2, reducing denoising from 200 steps to eight. Because published few-step results use different benchmarks and scoring procedures, we compare both distilled models with their teachers and other few-step methods under a common evaluation protocol covering structural accuracy, interface quality, ligand placement, and stereochemical validity. Blitz retains teacher-level structural accuracy, with AF3-Blitz outperforming all evaluated few-step baselines and exceeding its teacher on several accuracy metrics.
PDF arXiv
arXiv cond-mat.mtrl-sciOct 16
Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling
Yicheng Chen, Xiaoyun Yu, Hyejeong Song, Xintian Wang, Zihui Li +3
Accurate atomistic simulations of materials under electric fields require long-range electrostatics and electrical-response modeling at length scales beyond the reach of conventional first-principles methods. Yet these capabilities remain difficult to reconcile with the scalability of machine-learning interatomic potentials (MLIPs). Strictly local models enable efficient spatial parallelization but neglect long-range interactions, whereas existing electrostatic extensions introduce global dependencies that hinder distributed computation. Here, we overcome this limitation by analytically separating the long-range electrostatic forces and Born effective charges (BECs) into a global electrostatics calculation and strictly local derivatives of learned latent charges. This formulation preserves the spatial decomposability of equivariant MLIPs while enabling distributed evaluation of energies, forces, stresses, and BECs across different boundary conditions. Implemented as DPA4C-LR on the \texttt{nvalchemi} platform, the framework scales to 5.2 million atoms on four GPUs at approximately twice the computational cost of its short-range backbone. Trained on energies and forces, DPA4C-LR…
PDF arXiv
arXiv cs.AIOct 16
GEMS: Molecular Structure Elucidation as Learned Search over Isomer Graphs
Utku Umur Acikalin, Yingheng Wang, Goncalo J. Gouveia, Tyler J. Schwertfeger, Frank C. Schroeder +1
Identifying small-molecule structures from molecular formulas and tandem mass spectra remains a bottleneck in untargeted metabolomics and natural-product discovery. Even when database retrieval or de novo prediction misses the exact structure, the resulting candidates can provide starting points for exploring nearby structures through a few bond edits. We introduce GEMS (Guided Editing of Molecular Structures), which refines candidates through learned 2-switch bond edits that preserve the molecular formula and each atom's total valence. GEMS uses MIST, a pretrained spectrum-to-fingerprint model, to predict fingerprint probabilities once per spectrum. A neural policy compares each candidate's fingerprint with these predictions, links present features to the atoms and bonds that generate them, and scores all valid edits in one forward pass. The policy is trained on shortest edit paths between structures, so it can be pretrained on PubChem isomers without spectra and then fine-tuned with spectrum-predicted fingerprints. At inference, GEMS searches from multiple initial candidates matching the supplied molecular formula and ranks candidates by fingerprint agreement and multi-start…
PDF arXiv
arXiv cs.CVOct 16
LithoWorld: An Endpoint World Model for Lithography Process-Window Recovery
Botong Zhao, Fang Yu, Xinyuan Chen, Yue Lu
Lithography process drift can move a fabricated pattern outside its process window, producing unqualified scanning electron microscopy (SEM) morphology. We present LithoWorld, an endpoint world model for SEM-guided parameter recovery. Interior representative targets jointly determine qualified reference appearance and correction supervision. A causal predictive branch aligns currentimage features with endpoint representations, while a conditional flow policy generates a two-dimensional correction as an action chunk. The model learns from 173 groups of measured SEMs organized as focus-exposure maps, preserving unmeasured cells as unknown. On 200 test pairs from the 73-group subset, LithoWorld achieves 88.5% qualified-region success and 46.0% interior success, exceeding the strongest comparison method by 2.5 and 6.0 percentage points, respectively. Ablations separate the effects of target selection, endpoint alignment, and output parameterization. Qualitative SEM retrieval illustrates recovery to qualified interiors without requiring exact reference-coordinate matching.
PDF arXiv