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

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

21 September 2022
Peter K. Eastman
P. Behara
David L. Dotson
Raimondas Galvelis
John E. Herr
Joshua T. Horton
Yuezhi Mao
J. Chodera
Benjamin P. Pritchard
Yuanqing Wang
Gianni de Fabritiis
T. Markland
ArXivPDFHTML

Papers citing "SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials"

35 / 35 papers shown
Title
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Yasir Ghunaim
Andrés Villa
Gergo Ignacz
Gyorgy Szekely
Motasem Alfarra
Bernard Ghanem
AI4CE
84
0
0
28 Apr 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron J. Havens
Benjamin Kurt Miller
Bing Yan
Carles Domingo-Enrich
Anuroop Sriram
...
Brandon Amos
Brian Karrer
Xiang Fu
Guan-Horng Liu
Ricky T. Q. Chen
DiffM
43
0
0
16 Apr 2025
Universally applicable and tunable graph-based coarse-graining for Machine learning force fields
Universally applicable and tunable graph-based coarse-graining for Machine learning force fields
Christoph Brunken
Sebastien Boyer
Mustafa Omar
Martin Maarand
Olivier Peltre
Solal Attias
Bakary Diallo
Anastasia Markina
Olaf Othersen
Oliver E. Bent
OOD
AI4CE
44
0
0
24 Mar 2025
Strain Problems got you in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations
Strain Problems got you in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations
Ewan R. S. Wallace
Nathan C. Frey
Joshua A. Rackers
35
0
0
17 Mar 2025
Learning charges and long-range interactions from energies and forces
Learning charges and long-range interactions from energies and forces
Dongjin Kim
Daniel S. King
Peichen Zhong
Bingqing Cheng
79
4
0
19 Dec 2024
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Filippo Bigi
Marcel F. Langer
Michele Ceriotti
AI4CE
79
6
0
16 Dec 2024
Implicit Delta Learning of High Fidelity Neural Network Potentials
Implicit Delta Learning of High Fidelity Neural Network Potentials
Stephan Thaler
Cristian Gabellini
Nikhil Shenoy
Prudencio Tossou
AI4CE
70
0
0
08 Dec 2024
OpenQDC: Open Quantum Data Commons
OpenQDC: Open Quantum Data Commons
Cristian Gabellini
Nikhil Shenoy
Stephan Thaler
Semih Cantürk
Daniel McNeela
Dominique Beaini
Michael Bronstein
Prudencio Tossou
AI4CE
67
1
0
29 Nov 2024
The Importance of Being Scalable: Improving the Speed and Accuracy of
  Neural Network Interatomic Potentials Across Chemical Domains
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
Eric Qu
Aditi S. Krishnapriyan
LRM
18
10
0
31 Oct 2024
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
Luis Barroso-Luque
Muhammed Shuaibi
Xiang Fu
Brandon M. Wood
Misko Dzamba
Meng Gao
Ammar Rizvi
C. L. Zitnick
Zachary W. Ulissi
AI4CE
PINN
21
16
0
16 Oct 2024
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task
  Learning
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
Yuxuan Ren
Dihan Zheng
Chang-Shu Liu
Peiran Jin
Yu Shi
Lin Huang
Jiyan He
Shengjie Luo
Tao Qin
Tie-Yan Liu
AI4CE
25
1
0
14 Oct 2024
On the design space between molecular mechanics and machine learning
  force fields
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang
Kenichiro Takaba
Michael S. Chen
Marcus Wieder
Yuzhi Xu
...
Kyunghyun Cho
Joe G. Greener
Peter K. Eastman
Stefano Martiniani
M. Tuckerman
AI4CE
29
4
0
03 Sep 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Makoto Takamoto
Viktor Zaverkin
Mathias Niepert
AI4CE
52
0
0
23 Jul 2024
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Peter K. Eastman
Benjamin P. Pritchard
J. Chodera
T. Markland
22
10
0
18 Jun 2024
Diffusion Models in $\textit{De Novo}$ Drug Design
Diffusion Models in De Novo\textit{De Novo}De Novo Drug Design
Amira Alakhdar
Barnabás Póczos
Newell Washburn
MedIm
21
11
0
07 Jun 2024
FeNNol: an Efficient and Flexible Library for Building
  Force-field-enhanced Neural Network Potentials
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
Thomas Plé
Olivier Adjoua
Louis Lagardère
Jean‐Philip Piquemal
19
7
0
02 May 2024
Grappa -- A Machine Learned Molecular Mechanics Force Field
Grappa -- A Machine Learned Molecular Mechanics Force Field
Leif Seute
Eric Hartmann
Jan Stühmer
Frauke Gräter
27
3
0
25 Mar 2024
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular
  Simulations
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Raúl P. Peláez
Guillem Simeon
Raimondas Galvelis
Antonio Mirarchi
Peter K. Eastman
Stefan Doerr
Philipp Thölke
T. Markland
Gianni de Fabritiis
AI4CE
19
12
0
27 Feb 2024
ZnTrack -- Data as Code
ZnTrack -- Data as Code
Fabian Zills
M. Schäfer
S. Tovey
Johannes Kästner
Christian Holm
25
1
0
19 Jan 2024
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by
  Thorough Reproduction
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction
Alexander Telepov
Artem Tsypin
Kuzma Khrabrov
Sergey Yakukhnov
Pavel Strashnov
...
Egor Rumiantsev
Daniel Ezhov
Manvel Avetisian
Olga Popova
Artur Kadurin
22
4
0
18 Jan 2024
Gradual Optimization Learning for Conformational Energy Minimization
Gradual Optimization Learning for Conformational Energy Minimization
Artem Tsypin
L. Ugadiarov
Kuzma Khrabrov
Alexander Telepov
Egor Rumiantsev
Alexey Skrynnik
Aleksandr I. Panov
Dmitry Vetrov
E. Tutubalina
Artur Kadurin
13
1
0
05 Nov 2023
Generating QM1B with PySCF$_{\text{IPU}}$
Generating QM1B with PySCFIPU_{\text{IPU}}IPU​
Alexander Mathiasen
Hatem Helal
Kerstin Klaser
Paul Balanca
Josef Dean
Carlo Luschi
Dominique Beaini
Andrew Fitzgibbon
Dominic Masters
12
1
0
02 Nov 2023
Role of Structural and Conformational Diversity for Machine Learning
  Potentials
Role of Structural and Conformational Diversity for Machine Learning Potentials
Nikhil Shenoy
Prudencio Tossou
Emmanuel Noutahi
Hadrien Mary
Dominique Beaini
Jiarui Ding
AI4CE
14
0
0
30 Oct 2023
From Molecules to Materials: Pre-training Large Generalizable Models for
  Atomic Property Prediction
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi
Adeesh Kolluru
John R. Kitchin
Zachary W. Ulissi
C. L. Zitnick
Brandon M. Wood
AI4CE
17
30
0
25 Oct 2023
Towards Foundational Models for Molecular Learning on Large-Scale
  Multi-Task Datasets
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini
Shenyang Huang
Joao Alex Cunha
Zhiyi Li
Gabriela Moisescu-Pareja
...
Thérence Bois
Andrew Fitzgibbon
Bla.zej Banaszewski
Chad Martin
Dominic Masters
AI4CE
15
19
0
06 Oct 2023
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter K. Eastman
Raimondas Galvelis
Raúl P. Peláez
C. Abreu
Stephen E. Farr
...
Yuanqing Wang
Ivy Zhang
J. Chodera
Gianni de Fabritiis
T. Markland
AI4CE
VLM
15
36
0
04 Oct 2023
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials
  Modeling
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Kin Long Kelvin Lee
Carmelo Gonzales
Marcel Nassar
Matthew Spellings
Mikhail Galkin
Santiago Miret
19
15
0
12 Sep 2023
TensorNet: Cartesian Tensor Representations for Efficient Learning of
  Molecular Potentials
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
Guillem Simeon
Gianni de Fabritiis
11
43
0
10 Jun 2023
CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine
  Learning
CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning
Colin A. Grambow
Hayley Weir
Christian N Cunningham
Tommaso Biancalani
Kangway V Chuang
13
3
0
14 May 2023
Scaling the leading accuracy of deep equivariant models to biomolecular
  simulations of realistic size
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
Albert Musaelian
A. Johansson
Simon L. Batzner
Boris Kozinsky
14
48
0
20 Apr 2023
Denoise Pretraining on Nonequilibrium Molecules for Accurate and
  Transferable Neural Potentials
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials
Yuyang Wang
Chang Xu
Zijie Li
A. Farimani
AAML
AI4CE
11
20
0
03 Mar 2023
EspalomaCharge: Machine learning-enabled ultra-fast partial charge
  assignment
EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment
Yuanqing Wang
Iván Pulido
Kenichiro Takaba
Benjamin Kaminow
Jenke Scheen
Lily Wang
J. Chodera
13
17
0
14 Feb 2023
Forces are not Enough: Benchmark and Critical Evaluation for Machine
  Learning Force Fields with Molecular Simulations
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
Xiang Fu
Zhenghao Wu
Wujie Wang
T. Xie
S. Keten
Rafael Gómez-Bombarelli
Tommi Jaakkola
15
136
0
13 Oct 2022
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and
  Nonlocal Effects
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke
Stefan Chmiela
M. Gastegger
Kristof T. Schütt
H. E. Sauceda
K. Müller
142
242
0
01 May 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon L. Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
J. Mailoa
M. Kornbluth
N. Molinari
Tess E. Smidt
Boris Kozinsky
188
1,218
0
08 Jan 2021
1