Cecilia Clementi
#80,591
Most Influential Person Now
Biophysicist
Cecilia Clementi's AcademicInfluence.com Rankings
Cecilia Clementiphysics Degrees
Physics
#5590
World Rank
#7443
Historical Rank
#1651
USA Rank
Biophysics
#317
World Rank
#329
Historical Rank
#55
USA Rank
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Physics
Cecilia Clementi's Degrees
- PhD Biophysics University of California, Berkeley
- Masters Physics University of California, Berkeley
- Bachelors Physics University of California, Berkeley
Why Is Cecilia Clementi Influential?
(Suggest an Edit or Addition)According to Wikipedia, Cecilia Clementi is an Italian-American scientist who specialises in the simulation of biomolecules. She is a Professor of Computational Biophysics at the Free University of Berlin. She was previously a Professor of Chemistry at the Rice University and co-director of the National Science Foundation Molecular Sciences Software Institute. From 2017 to 2019 she held an Einstein Foundation fellowship.
Cecilia Clementi's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Topological and energetic factors: what determines the structural details of the transition state ensemble and "en-route" intermediates for protein folding? An investigation for small globular proteins. (2000) (1102)
- Machine learning for molecular simulation (2019) (341)
- Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction (2006) (292)
- Coarse-grained models of protein folding: toy models or predictive tools? (2008) (291)
- Machine Learning of Coarse-Grained Molecular Dynamics Force Fields (2018) (254)
- Determination of reaction coordinates via locally scaled diffusion map. (2011) (235)
- AWSEM-MD: protein structure prediction using coarse-grained physical potentials and bioinformatically based local structure biasing. (2012) (234)
- Quantifying the roughness on the free energy landscape: entropic bottlenecks and protein folding rates. (2004) (212)
- How native-state topology affects the folding of dihydrofolate reductase and interleukin-1beta. (2000) (197)
- Discovering mountain passes via torchlight: methods for the definition of reaction coordinates and pathways in complex macromolecular reactions. (2013) (175)
- Interplay among tertiary contacts, secondary structure formation and side-chain packing in the protein folding mechanism: all-atom representation study of protein L. (2003) (166)
- The effects of nonnative interactions on protein folding rates: Theory and simulation (2004) (152)
- Kinetic distance and kinetic maps from molecular dynamics simulation. (2015) (148)
- Sparse learning of stochastic dynamical equations. (2017) (129)
- Dynamics of polymer translocation through nanopores: theory meets experiment. (2006) (123)
- Jagged–Delta asymmetry in Notch signaling can give rise to a Sender/Receiver hybrid phenotype (2015) (114)
- Data-driven approximation of the Koopman generator: Model reduction, system identification, and control (2019) (112)
- From coarse‐grain to all‐atom: Toward multiscale analysis of protein landscapes (2007) (111)
- Adaptive resolution simulation of liquid water (2006) (105)
- GEOMETRY OF DYNAMICS, LYAPUNOV EXPONENTS, AND PHASE TRANSITIONS (1997) (104)
- Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods. (2017) (103)
- Prediction of folding mechanism for circular-permuted proteins. (2001) (96)
- Unsupervised Learning Methods for Molecular Simulation Data (2021) (94)
- Fast recovery of free energy landscapes via diffusion-map-directed molecular dynamics. (2014) (89)
- Balancing energy and entropy: a minimalist model for the characterization of protein folding landscapes. (2005) (88)
- Machine learning for protein folding and dynamics. (2019) (86)
- Modeling protein conformational ensembles: From missing loops to equilibrium fluctuations (2006) (82)
- Combining experimental and simulation data of molecular processes via augmented Markov models (2017) (76)
- Multiscale characterization of protein conformational ensembles (2009) (74)
- Coarse graining molecular dynamics with graph neural networks. (2020) (73)
- Rapid exploration of configuration space with diffusion-map-directed molecular dynamics. (2013) (72)
- Modeling diffusive dynamics in adaptive resolution simulation of liquid water. (2007) (70)
- Perspective: Computational chemistry software and its advancement as illustrated through three grand challenge cases for molecular science. (2018) (66)
- Optimal combination of theory and experiment for the characterization of the protein folding landscape of S6: how far can a minimalist model go? (2004) (66)
- Communication: On the locality of hydrogen bond networks at hydrophobic interfaces. (2010) (59)
- Application of nonlinear dimensionality reduction to characterize the conformational landscape of small peptides (2010) (58)
- Minimalist protein model as a diagnostic tool for misfolding and aggregation. (2006) (55)
- TorchMD: A Deep Learning Framework for Molecular Simulations (2020) (54)
- Commute Maps: Separating Slowly Mixing Molecular Configurations for Kinetic Modeling. (2016) (51)
- Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias (2017) (50)
- Polymer reversal rate calculated via locally scaled diffusion map. (2011) (49)
- Fast and reliable analysis of molecular motion using proximity relations and dimensionality reduction (2007) (49)
- Investigating Molecular Kinetics by Variationally Optimized Diffusion Maps. (2015) (48)
- Characterization of the folding landscape of monomeric lactose repressor: quantitative comparison of theory and experiment. (2005) (47)
- FOLDING, DESIGN, AND DETERMINATION OF INTERACTION POTENTIALS USING OFF-LATTICE DYNAMICS OF MODEL HETEROPOLYMERS (1998) (43)
- Quantitative comparison of adaptive sampling methods for protein dynamics. (2018) (43)
- Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach. (2020) (39)
- Operating principles of Notch–Delta–Jagged module of cell–cell communication (2015) (39)
- Delineation of folding pathways of a β-sheet miniprotein. (2011) (39)
- On the characterization of protein native state ensembles. (2007) (37)
- Restriction versus guidance in protein structure prediction (2009) (34)
- Geometry of dynamics and phase transitions in classical lattice phi^4 theories (1997) (33)
- Learning Effective Molecular Models from Experimental Observables. (2018) (32)
- Determination of interaction potentials of amino acids from native protein structures: Tests on simple lattice models (1998) (31)
- The experimental folding landscape of monomeric lactose repressor, a large two-domain protein, involves two kinetic intermediates. (2005) (29)
- Unfolding the fold of cyclic cysteine‐rich peptides (2008) (27)
- Graphene, other carbon nanomaterials and the immune system: toward nanoimmunity-by-design (2020) (27)
- Surveying biomolecular frustration at atomic resolution (2020) (25)
- A tripodal peptide ligand for asymmetric Rh(II) catalysis highlights unique features of on-bead catalyst development (2014) (25)
- Coarse-graining molecular systems by spectral matching. (2019) (24)
- Machine learning meets chemical physics. (2021) (23)
- APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations (2019) (23)
- Folding Lennard‐Jones proteins by a contact potential (1999) (22)
- Introduction: Machine Learning at the Atomic Scale. (2021) (21)
- Molecular recognition of DNA by ligands: roughness and complexity of the free energy profile. (2013) (21)
- ExTASY: Scalable and flexible coupling of MD simulations and advanced sampling techniques (2016) (20)
- Sampling Conformation Space to Model Equilibrium Fluctuations in Proteins (2007) (19)
- Machine learning implicit solvation for molecular dynamics. (2021) (19)
- A Data-Driven Perspective on the Hierarchical Assembly of Molecular Structures. (2018) (19)
- Extensible and Scalable Adaptive Sampling on Supercomputers. (2019) (18)
- Path integral-GC-AdResS simulation of a large hydrophobic solute in water: a tool to investigate the interplay between local microscopic structures and quantum delocalization of atoms in space. (2017) (17)
- Multi-body effects in a coarse-grained protein force field. (2021) (17)
- Mapping folding energy landscapes with theory and experiment. (2008) (17)
- Size and topology modulate the effects of frustration in protein folding (2018) (16)
- Rapid Calculation of Molecular Kinetics Using Compressed Sensing. (2018) (15)
- On the origin of phase transitions in the absence of symmetry-breaking (2017) (15)
- Protein design is a key factor for subunit-subunit association. (1999) (15)
- Nanoscale coupling of endocytic pit growth and stability (2019) (14)
- Localizing Frustration in Proteins Using All-Atom Energy Functions. (2019) (13)
- Rapid assessment of T-cell receptor specificity of the immune repertoire (2020) (12)
- Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests (2020) (12)
- Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer (2019) (11)
- Markov state modeling reveals alternative unbinding pathways for peptide–MHC complexes (2020) (11)
- A comparative analysis of clustering algorithms: O2 migration in truncated hemoglobin I from transition networks. (2015) (11)
- Supersymmetric Langevin equation to explore free-energy landscapes. (2006) (10)
- A Geometric Interpretation of Integrable Motions (2001) (10)
- Spectral Properties of Effective Dynamics from Conditional Expectations (2019) (10)
- A new perspective on transition states: χ1 separatrix. (2011) (9)
- Force-matching Coarse-Graining without Forces (2022) (8)
- The effects of non-native interactions on protein folding rates: Theory and simulation (2004) (8)
- Tensor-based computation of metastable and coherent sets (2019) (7)
- Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces. (2022) (6)
- Think Globally, Move Locally: Coarse Graining of Effective Free Energy Surfaces (2011) (4)
- Skipping the Replica Exchange Ladder with Normalizing Flows. (2022) (4)
- Hamiltonian dynamics of homopolymer chain models. (2006) (4)
- Quantum dynamics using path integral coarse-graining. (2022) (4)
- Multiscale Approach to the Determination of the Photoactive Yellow Protein Signaling State Ensemble (2014) (4)
- Machine Learning Coarse-Grained Potentials of Protein Thermodynamics (2022) (3)
- Deep learning to decompose macromolecules into independent Markovian domains (2022) (3)
- Tensor-based EDMD for the Koopman analysis of high-dimensional systems (2019) (3)
- ExTASY: A python-based Extensible Toolkit for Advanced Sampling and Analysis in Biomolecular Simulation (2015) (3)
- Fast track to structural biology (2021) (2)
- Adaptive Resolution in Molecular Dynamics Simulations (2007) (2)
- The Effect of Electrostatic Interactions on the Folding Kinetics of a 3-α-Helical Bundle Protein Family. (2018) (2)
- Machine learned coarse-grained protein force-fields: Are we there yet? (2023) (2)
- Effective Potentials for Protein Folding Models (1998) (2)
- Preface: Special Topic on Reaction Pathways. (2017) (1)
- Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics (2023) (1)
- Multiscale characterization of macromolecular dynamics: application to photoacitve yellow protein (2013) (1)
- Publisher's Note: “Molecular recognition of DNA by ligands: Roughness and complexity of the free energy profile” [J. Chem. Phys. 139, 145102 (2013)] (2013) (1)
- Slicing and Dicing: Optimal Coarse-Grained Representation to Preserve Molecular Kinetics (2023) (1)
- Putting ExTASY in charge of an arduous computational challenge (2014) (1)
- Machine Learning Implicit Solvation for Molecular Dynamicsa) (2021) (0)
- Characterization of Protein-Folding Landscapes by Coarse-Grained Models Incorporating Experimental Data (2008) (0)
- Adaptive sampling strategies with high-throughput molecular dynamics (2017) (0)
- Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics. (2023) (0)
- Multiscale Modeling of Biomolecular Processes by Combining Experiment and Simulation (2019) (0)
- Multiscale modeling of macromolecular dynamics (2013) (0)
- EnGens: a computational framework for generation and analysis of representative protein conformational ensembles (2023) (0)
- AI3SD Video: Designing molecular models by machine learning and experimental data (2021) (0)
- 9 A : Reference B : Full regression C : Sparse regression D : Timescales (2019) (0)
- Multi-resolution protein modeling by combining theory and experiment (2009) (0)
- Prediction of protein functional states by multi-resolution protein modeling (2009) (0)
- White Paper: “Machine Learning for Physics and the Physics of Learning” (IPAM Long Program, Fall 2019) (2019) (0)
- M ay 2 00 1 A geometric interpretation of integrable motions (2001) (0)
- Deep Spectral Coarse Graining: Learning Simple, Dynamically Consistent Protein Models (2020) (0)
- Simulations Reveal Multiple Intermediates in the Unzipping Mechanism of Neuronal SNARE Complex. (2018) (0)
- Incorporating experimental data into long timescale simulations of macromolecules (2019) (0)
- Fast and Reliable Analysis of Molecular Motion Using Proximity Relations and Dimensionality Reduction Research Article Authors (2007) (0)
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