Training a force field for proteins and small molecules from scratch | Paper Citation Record

Observation 8104805f-3180-4e5e-961e-239f8bb00fc0 · outbound

This paper cites Fixed-Charge Atomistic Force Fields for Molecular Dynamics Simulations in the Condensed Phase: An Overview,.

Training a force field for proteins and small molecules from scratch Fixed-Charge Atomistic Force Fields for Molecular Dynamics Simulations in the Condensed Phase: An Overview,

Reference 1

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Observation b375bb92-00d7-4dfc-b300-b86468536392 · outbound

This paper cites Recent Developments in Amber Biomolecular Simulations,.

Training a force field for proteins and small molecules from scratch Recent Developments in Amber Biomolecular Simulations,

Reference 2

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Observation 4fe2ecac-a5bb-41f2-9d3e-6b0c975857a1 · outbound

This paper cites CHARMM: The biomolecular simulation program,.

Training a force field for proteins and small molecules from scratch CHARMM: The biomolecular simulation program,

Reference 3

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Observation a44e071f-8477-41d3-a489-535d8518e6ee · outbound

This paper cites Developing a molecular dynamics force field for both folded and disordered protein states,.

Training a force field for proteins and small molecules from scratch Developing a molecular dynamics force field for both folded and disordered protein states,

Reference 4

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Observation b13e9cfa-561c-44ef-adc9-ec989ecae4ee · outbound

This paper cites CHARMM36m: an improved force field for folded and intrinsically disordered proteins,.

Training a force field for proteins and small molecules from scratch CHARMM36m: an improved force field for folded and intrinsically disordered proteins,

Reference 5

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Observation d51ec08c-639f-4fe4-bf98-548999232090 · outbound

This paper cites Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15,.

Training a force field for proteins and small molecules from scratch Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15,

Reference 6

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Observation fac50b18-cb02-4be5-b45a-504c5fd99b47 · outbound

This paper cites Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field,.

Training a force field for proteins and small molecules from scratch Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field,

Reference 7

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Observation 700ce9ca-7e12-4603-96ca-9329d431b73f · outbound

This paper cites Systematic design of biomolecular force fields,.

Training a force field for proteins and small molecules from scratch Systematic design of biomolecular force fields,

Reference 8

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Observation e2b602d4-40fe-4936-80dd-40a32ba4039d · outbound

This paper cites Accurate machine learning force fields via experimental and simulation data fusion,.

Training a force field for proteins and small molecules from scratch Accurate machine learning force fields via experimental and simulation data fusion,

Reference 9

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Observation 31ad7503-3fd4-4702-ad70-39b53a476360 · outbound

This paper cites Toward empirical force fields that match experimental observables,.

Training a force field for proteins and small molecules from scratch Toward empirical force fields that match experimental observables,

Reference 10

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Observation 2aa5811b-95b6-4b53-9781-f916fb94837b · outbound

This paper cites On the design space between molecular mechanics and machine learning force fields,.

Training a force field for proteins and small molecules from scratch On the design space between molecular mechanics and machine learning force fields,

Reference 11

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Observation b8fe5fbc-9800-415a-acbe-ec5696208de7 · outbound

This paper cites How fast-folding proteins fold,.

Training a force field for proteins and small molecules from scratch How fast-folding proteins fold,

Reference 12

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Observation aa1c4648-08d7-4f7b-82b4-5ecda17e4da8 · outbound

This paper cites Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field,.

Training a force field for proteins and small molecules from scratch Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field,

Reference 13

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Observation 32f0ba8e-1318-4c52-8365-d7167b6c0ac3 · outbound

This paper cites Large- scale collaborative assessment of binding free energy calculations for drug discovery using OpenFE,.

Training a force field for proteins and small molecules from scratch Large- scale collaborative assessment of binding free energy calculations for drug discovery using OpenFE,

Reference 14

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Observation c4d5a8c7-dfd7-46a8-ad6f-1cedb515b6f0 · outbound

This paper cites Machine- learned molecular mechanics force fields from large-scale quantum chemical data,.

Training a force field for proteins and small molecules from scratch Machine- learned molecular mechanics force fields from large-scale quantum chemical data,

Reference 15

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Observation e44a58f2-f75f-4d13-b307-ad2e71c59716 · outbound

This paper cites End-to-end differentiable construction of molecular mechanics force fields,.

Training a force field for proteins and small molecules from scratch End-to-end differentiable construction of molecular mechanics force fields,

Reference 16

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Observation 428f69c2-07d9-4471-9f36-a0628991fe01 · outbound

This paper cites Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions,.

Training a force field for proteins and small molecules from scratch Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions,

Reference 17

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Observation cb5bdb23-9000-4eab-bc1b-df8f2e802840 · outbound

This paper cites Grappa – a machine learned molecular mechanics force field,.

Training a force field for proteins and small molecules from scratch Grappa – a machine learned molecular mechanics force field,

Reference 18

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Observation 890488ca-5173-42b0-adc4-5769897609a2 · outbound

This paper cites Con- densation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications,.

Training a force field for proteins and small molecules from scratch Con- densation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications,

Reference 19

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Observation c1bb3ff2-5e07-4b00-8c68-3d77f2949885 · outbound

This paper cites Advancing Force Fields Pa- rameterization: A Directed Graph Attention Networks Approach,.

Training a force field for proteins and small molecules from scratch Advancing Force Fields Pa- rameterization: A Directed Graph Attention Networks Approach,

Reference 20

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Observation cabdba39-b981-4b89-958d-d8c3e90daf44 · outbound

This paper cites Atom typing using graph representation learning: How do models learn chemistry?,.

Training a force field for proteins and small molecules from scratch Atom typing using graph representation learning: How do models learn chemistry?,

Reference 21

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Observation 6cd56164-eece-44f5-9d4d-aea55caacd9b · outbound

This paper cites Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage,.

Training a force field for proteins and small molecules from scratch Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage,

Reference 22

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Observation 27b524ed-8d66-4315-afce-ae5f5c639f5f · outbound

This paper cites Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field.

Training a force field for proteins and small molecules from scratch Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field

Reference 23

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Observation e58919ce-26ff-484b-bc40-5bf5024e2818 · outbound

This paper cites Building Force Fields: An Automatic, Systematic, and Reproducible Approach,.

Training a force field for proteins and small molecules from scratch Building Force Fields: An Automatic, Systematic, and Reproducible Approach,

Reference 24

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Observation 40a447a5-41ba-4667-bdd5-981acb697070 · outbound

This paper cites Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting,.

Training a force field for proteins and small molecules from scratch Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting,

Reference 25

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Observation d0b797a7-8d9e-4181-b6d4-5c1747e446de · outbound

This paper cites A double exponential potential for van der Waals interaction,.

Training a force field for proteins and small molecules from scratch A double exponential potential for van der Waals interaction,

Reference 26

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Observation 29500b1d-8e6f-44cc-a7ab-21fb97e8f8da · outbound

This paper cites A transferable double exponential potential for condensed phase simulations of small molecules,.

Training a force field for proteins and small molecules from scratch A transferable double exponential potential for condensed phase simulations of small molecules,

Reference 27

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Observation 17227552-4a0a-4763-9746-1d77a6b1792e · outbound

This paper cites Accurate and Efficient Corrections for Missing Dispersion Interactions in Molecular Simulations,.

Training a force field for proteins and small molecules from scratch Accurate and Efficient Corrections for Missing Dispersion Interactions in Molecular Simulations,

Reference 28

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Observation 5749cb08-7417-401e-9973-214a197ab800 · outbound

This paper cites On the Role of London Dispersion Forces in Biomolecular Structure Determi- nation,.

Training a force field for proteins and small molecules from scratch On the Role of London Dispersion Forces in Biomolecular Structure Determi- nation,

Reference 29

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Observation 2c66ddca-936c-497d-a434-720ccc148823 · outbound

This paper cites The PHAST 2.0 Force Field for General Small Molecule and Materials Simu- lations,.

Training a force field for proteins and small molecules from scratch The PHAST 2.0 Force Field for General Small Molecule and Materials Simu- lations,

Reference 30

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Observation 1eed633e-6b28-44fc-9c4d-e138a999f3ea · outbound

This paper cites Perspective: Advances and challenges in treating van der Waals dispersion forces in density functional theory,.

Training a force field for proteins and small molecules from scratch Perspective: Advances and challenges in treating van der Waals dispersion forces in density functional theory,

Reference 31

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Observation 88f4671e-bea7-4fdd-9416-24e6e15c1048 · outbound

This paper cites Optimized Lennard-Jones Parameters for Druglike Small Molecules,.

Training a force field for proteins and small molecules from scratch Optimized Lennard-Jones Parameters for Druglike Small Molecules,

Reference 32

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Observation b7035754-d51d-466d-b9b1-c7215ba386d7 · outbound

This paper cites Beyond Born–Mayer: Improved Models for Short-Range Repulsion in ab Initio Force Fields,.

Training a force field for proteins and small molecules from scratch Beyond Born–Mayer: Improved Models for Short-Range Repulsion in ab Initio Force Fields,

Reference 33

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Observation 9e416652-614e-4cf4-b675-9fc7e8079bd2 · outbound

This paper cites Accelerating Development and Execution Speed with Just-in-Time GPU Code Gener- ation,.

Training a force field for proteins and small molecules from scratch Accelerating Development and Execution Speed with Just-in-Time GPU Code Gener- ation,

Reference 34

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Observation 3981f5ea-e689-4dab-9e83-f72557115a06 · outbound

This paper cites OpenMM 7: Rapid development of high performance algorithms for molecular dynamics,.

Training a force field for proteins and small molecules from scratch OpenMM 7: Rapid development of high performance algorithms for molecular dynamics,

Reference 35

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Observation 1b92a58b-e9e9-4195-bb11-4489e3d08e23 · outbound

This paper cites Minimal Basis Iterative Stockholder: Atoms in Molecules for Force-Field Development,.

Training a force field for proteins and small molecules from scratch Minimal Basis Iterative Stockholder: Atoms in Molecules for Force-Field Development,

Reference 36

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Observation 7c3923cb-bec9-458c-84a4-39c964abceaa · outbound

This paper cites SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials,.

Training a force field for proteins and small molecules from scratch SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials,

Reference 37

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Observation 1dce8f3a-42ac-41aa-b4da-948cf82b9f99 · outbound

This paper cites Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning,.

Training a force field for proteins and small molecules from scratch Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning,

Reference 38

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Observation 1983a5cd-1c2e-454d-80c3-8c61b49cd056 · outbound

This paper cites MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules,.

Training a force field for proteins and small molecules from scratch MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules,

Reference 39

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Observation 00b89791-6b66-411b-b5c5-c43f7347236a · outbound

This paper cites QC Optimization Dataset: OpenFF Industry Benchmark Season 1 v1.2,.

Training a force field for proteins and small molecules from scratch QC Optimization Dataset: OpenFF Industry Benchmark Season 1 v1.2,

Reference 40

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Observation 31db9c14-e5c8-48dc-bc96-df71e03530e8 · outbound

This paper cites TFD: torsion fingerprints as a new measure to compare small molecule conformations,.

Training a force field for proteins and small molecules from scratch TFD: torsion fingerprints as a new measure to compare small molecule conformations,

Reference 41

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Observation 52435920-028c-4fdd-a723-3dcf1521f61a · outbound

This paper cites Development of a Force Field for the Simulation of Single-Chain Proteins and Protein-Protein Complexes,.

Training a force field for proteins and small molecules from scratch Development of a Force Field for the Simulation of Single-Chain Proteins and Protein-Protein Complexes,

Reference 42

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Observation a069ab2f-dd27-484f-871c-663490767f5e · outbound

This paper cites Differentiable simulation to develop molecular dynamics force fields for disordered proteins,.

Training a force field for proteins and small molecules from scratch Differentiable simulation to develop molecular dynamics force fields for disordered proteins,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:54.584911Z

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Observation bf8edcca-3f32-4412-a01f-bb1aa8eea451 · outbound

This paper cites Molecular Basis of Small-Molecule Binding toα-Synuclein,.

Training a force field for proteins and small molecules from scratch Molecular Basis of Small-Molecule Binding toα-Synuclein,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:54.733421Z

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Observation 183a8ebb-e6f5-4efd-a2e6-6dc8d0725649 · outbound

This paper cites Comparison of simple potential functions for simulating liquid water,.

Training a force field for proteins and small molecules from scratch Comparison of simple potential functions for simulating liquid water,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:54.909296Z

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Observation 5da7cec2-93b6-4576-9f28-f4cbd74c7f07 · outbound

This paper cites A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules,.

Training a force field for proteins and small molecules from scratch A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules,

Reference 46

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Observation 8b5f617e-6aef-4d63-858a-62a73466628f · outbound

This paper cites Open Force Field Evaluator: An Au- tomated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation,.

Training a force field for proteins and small molecules from scratch Open Force Field Evaluator: An Au- tomated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.106582Z

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Observation a98435cd-238c-47bf-a9a5-5f03e808a463 · outbound

This paper cites The representation of van der Waals (vdW) interactions in molecular mechanics force fields: potential form, combination rules, and vdW parameters,.

Training a force field for proteins and small molecules from scratch The representation of van der Waals (vdW) interactions in molecular mechanics force fields: potential form, combination rules, and vdW parameters,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.187564Z

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Observation c721ce9e-8ee0-403a-a21d-92020d76e87d · outbound

This paper cites The maximal and current accuracy of rigorous protein-ligand binding free energy calculations,.

Training a force field for proteins and small molecules from scratch The maximal and current accuracy of rigorous protein-ligand binding free energy calculations,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.272129Z

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Observation 5eb06ffb-4c72-44ff-8d49-b4f8ee51a7cb · outbound

This paper cites Best Practices for Constructing, Preparing, and Evaluating Protein- Ligand Binding Affinity Benchmarks [Article v1.0],.

Training a force field for proteins and small molecules from scratch Best Practices for Constructing, Preparing, and Evaluating Protein- Ligand Binding Affinity Benchmarks [Article v1.0],

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.326180Z

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Observation 0d9c0e84-11c7-4c4e-8f84-01bc006b2afc · outbound

This paper cites Bang for your Buck: Trading Off Utility with Time and Money for Non-Equilibrium Switching in Orion: https://www.youtube.com/watch?v=d76MZoxc2eo,.

Training a force field for proteins and small molecules from scratch Bang for your Buck: Trading Off Utility with Time and Money for Non-Equilibrium Switching in Orion: https://www.youtube.com/watch?v=d76MZoxc2eo,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.407103Z

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Observation 27cf739d-6c6c-4b71-bb65-9406b3ef80ec · outbound

This paper cites Influence of the Lennard-Jones Combination Rules on the Simulated Prop- erties of Organic Liquids at Optimal Force-Field Parametrization,.

Training a force field for proteins and small molecules from scratch Influence of the Lennard-Jones Combination Rules on the Simulated Prop- erties of Organic Liquids at Optimal Force-Field Parametrization,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.492935Z

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Observation b895893e-0076-4a03-8781-e39d0da4f9e4 · outbound

This paper cites New combining rules for rare gas van der waals parameters,.

Training a force field for proteins and small molecules from scratch New combining rules for rare gas van der waals parameters,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.550671Z

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Observation 10a5833d-4e21-4c13-b654-bca5e975a128 · outbound

This paper cites Inductive Representation Learning on Large Graphs,.

Training a force field for proteins and small molecules from scratch Inductive Representation Learning on Large Graphs,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.602732Z

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Observation 8bfc8d6c-5652-4fcb-b3a4-fe196759827d · outbound

This paper cites Open Force Field BespokeFit: Automat- ing Bespoke Torsion Parametrization at Scale,.

Training a force field for proteins and small molecules from scratch Open Force Field BespokeFit: Automat- ing Bespoke Torsion Parametrization at Scale,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.685534Z

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Observation 5d34600e-8108-41af-94f0-a9b50a320d91 · outbound

This paper cites The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling,.

Training a force field for proteins and small molecules from scratch The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.798463Z

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Observation 6b2bb277-55ca-4eca-8144-58abf641d097 · outbound

This paper cites Improved Treatment of 1–4 Interactions in Force Fields for Molecular Dynamics Simulations,.

Training a force field for proteins and small molecules from scratch Improved Treatment of 1–4 Interactions in Force Fields for Molecular Dynamics Simulations,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:55.915639Z

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Observation 10dca69f-7710-4603-a0f2-fc34dc2912e6 · outbound

This paper cites Force field development phase II: Relaxation of physics-based criteria... or inclusion of more rigorous physics into the representation of molecular energetics,.

Training a force field for proteins and small molecules from scratch Force field development phase II: Relaxation of physics-based criteria... or inclusion of more rigorous physics into the representation of molecular energetics,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.021607Z

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Observation aec87dee-29c5-4692-a6e7-4e4dc36a7f85 · outbound

This paper cites Tuning Potential Functions to Host-Guest Binding Data,.

Training a force field for proteins and small molecules from scratch Tuning Potential Functions to Host-Guest Binding Data,

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Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.142982Z

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Observation 6accca4a-e318-447a-adff-bcd49b392be8 · outbound

This paper cites Fine-tuning molecular mechanics force fields to experimental free energy measurements,.

Training a force field for proteins and small molecules from scratch Fine-tuning molecular mechanics force fields to experimental free energy measurements,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.214700Z

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Observation 76a6d221-7e9a-4b62-bd7a-9696fe418889 · outbound

This paper cites Statistically optimal analysis of samples from multiple equilibrium states,.

Training a force field for proteins and small molecules from scratch Statistically optimal analysis of samples from multiple equilibrium states,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.281083Z

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Observation b54dcb93-31eb-43ef-9f4c-b8f03ed4eaff · outbound

This paper cites Analyzing Atomic Interactions in Molecules as Learned by Neural Networks,.

Training a force field for proteins and small molecules from scratch Analyzing Atomic Interactions in Molecules as Learned by Neural Networks,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.360448Z

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Observation a49d19d4-49b6-487c-bf50-5f1ed8d10dab · outbound

This paper cites Escaping Atom Types in Force Fields Using Direct Chemical Perception,.

Training a force field for proteins and small molecules from scratch Escaping Atom Types in Force Fields Using Direct Chemical Perception,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.477257Z

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Observation 79fe976e-1c9a-4a9e-8f1b-4ea93574b646 · outbound

This paper cites Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs,.

Training a force field for proteins and small molecules from scratch Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.557644Z

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Observation 746c1ba9-409b-42c8-a0ef-ed61a6b11264 · outbound

This paper cites Robustness in the fitting of molecular mechanics parameters,.

Training a force field for proteins and small molecules from scratch Robustness in the fitting of molecular mechanics parameters,

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Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.671998Z

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Observation be925420-b8b7-4cfd-8307-681da04535d1 · outbound

This paper cites Biomolecular dynamics with machine-learned quantum- mechanical force fields trained on diverse chemical fragments,.

Training a force field for proteins and small molecules from scratch Biomolecular dynamics with machine-learned quantum- mechanical force fields trained on diverse chemical fragments,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.770930Z

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Observation 473872e0-45b6-4410-9345-0c72dabc2ff6 · outbound

This paper cites Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent,.

Training a force field for proteins and small molecules from scratch Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.855419Z

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Observation 029d6a28-85ca-40d4-a6cc-cf43da663935 · outbound

This paper cites Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0],.

Training a force field for proteins and small molecules from scratch Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0],

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:56.961990Z

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Observation af52e197-85cd-4f80-8759-699aea801b49 · outbound

This paper cites Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0],.

Training a force field for proteins and small molecules from scratch Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0],

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:57.133448Z

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Observation 5245babd-3553-42c8-a99e-bcb3a4a0177b · outbound

This paper cites Biological Magnetic Resonance Data Bank,.

Training a force field for proteins and small molecules from scratch Biological Magnetic Resonance Data Bank,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:57.249342Z

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Observation 682a83c5-0b87-4b19-975f-b153c8cefa62 · outbound

This paper cites Predicting chemical shifts with graph neural networks,.

Training a force field for proteins and small molecules from scratch Predicting chemical shifts with graph neural networks,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:57.338023Z

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Observation f60eb8a5-52fc-42e0-8673-78dbeaebbc70 · outbound

This paper cites Julia: A fresh approach to numerical computing,.

Training a force field for proteins and small molecules from scratch Julia: A fresh approach to numerical computing,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:57.501940Z

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Observation 0e2c724d-447b-4a9e-9246-f76558d25ca3 · outbound

This paper cites Julia for biologists,.

Training a force field for proteins and small molecules from scratch Julia for biologists,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:57.616798Z

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Observation c49e0205-af49-492c-b4a4-14c9fc532076 · outbound

This paper cites Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients,.

Training a force field for proteins and small molecules from scratch Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients,

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Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:57.719674Z

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Observation e98b9a24-9ff1-4480-8c1b-429c3a8ce094 · outbound

This paper cites Don't Unroll Adjoint: Differentiating SSA-Form Programs.

Training a force field for proteins and small molecules from scratch Don't Unroll Adjoint: Differentiating SSA-Form Programs

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:57.831626Z

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Observation e746dfb5-f80a-442a-8984-2746a8bd0301 · outbound

This paper cites Particle mesh Ewald: An N·log(N) method for Ewald sums in large systems,.

Training a force field for proteins and small molecules from scratch Particle mesh Ewald: An N·log(N) method for Ewald sums in large systems,

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Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:57.930576Z

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Observation ab5b1f82-94ab-46b2-95c1-d0cc37b91ae7 · outbound

This paper cites MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations,.

Training a force field for proteins and small molecules from scratch MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations,

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Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.030159Z

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Observation 9adf9e23-d1a1-4679-a559-905af76efeaf · outbound

This paper cites BioStructures.jl: read, write and manipulate macromolecular structures in Julia,.

Training a force field for proteins and small molecules from scratch BioStructures.jl: read, write and manipulate macromolecular structures in Julia,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.165029Z

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Observation f7fc47dc-1e70-42f3-9a3a-aa35bb636739 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library,.

Training a force field for proteins and small molecules from scratch PyTorch: An Imperative Style, High-Performance Deep Learning Library,

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Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.250547Z

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Observation fccce74c-0f9e-4df3-b453-b7ec1dbfd49f · outbound

This paper cites RDKit: Open-source cheminformatics. https://www.rdkit.org,.

Training a force field for proteins and small molecules from scratch RDKit: Open-source cheminformatics. https://www.rdkit.org,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.336372Z

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Observation 00cf0c63-cd5d-4e0d-b5f4-e3d43135ef89 · outbound

This paper cites Long-time-step molecular dynamics through hydrogen mass repartitioning,.

Training a force field for proteins and small molecules from scratch Long-time-step molecular dynamics through hydrogen mass repartitioning,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.410241Z

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Observation 16a34eab-58fd-4315-ac70-5cb0bd5c0470 · outbound

This paper cites proteinbenchmark: https://github.com/openforcefield/proteinbenchmark,.

Training a force field for proteins and small molecules from scratch proteinbenchmark: https://github.com/openforcefield/proteinbenchmark,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.501707Z

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Observation 06b922ea-6963-4ee0-87be-58b57d01ae2a · outbound

This paper cites OpenFreeEnergy/IndustryBenchmarks2024: v1.0.0,.

Training a force field for proteins and small molecules from scratch OpenFreeEnergy/IndustryBenchmarks2024: v1.0.0,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.588015Z

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Observation edf4cc90-0c2e-4a32-b381-7fdae98bc24b · outbound

This paper cites Lead optimization mapper: automating free energy calculations for lead optimization,.

Training a force field for proteins and small molecules from scratch Lead optimization mapper: automating free energy calculations for lead optimization,

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.760289Z

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Observation bb1d4126-673e-4c84-900f-84401b45dc31 · outbound

This paper cites Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations,.

Training a force field for proteins and small molecules from scratch Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations,

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Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:58.910330Z

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Observation 7edb2bd5-2874-42b1-b664-333e033feb51 · outbound

This paper cites Optimal Measurement Network of Pairwise Differences,.

Training a force field for proteins and small molecules from scratch Optimal Measurement Network of Pairwise Differences,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:59.004865Z

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Observation 3dce94f2-f127-4478-9e95-6c5e669614a7 · outbound

This paper cites Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method,.

Training a force field for proteins and small molecules from scratch Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method,

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Resolution: unresolved; no resolver link, observed 2026-08-02T18:02:59.186717Z

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