Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

Effect of <i>|Δd</i>_<i>n</i><i>|</i> perturbation on 3D modeling. by Giacomo Janson (8138517)

Modeling with uniform <i>σ</i> values. by Giacomo Janson (8138517)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. …”

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of <i>σ</i> values profoundly influences 3D modeling. …”

gmxapi usage examples. by M. Eric Irrgang (4487278)

PyPEFAn Integrated Framework for Data-Driven Protein Engineering by Niklas E. Siedhoff (11133851)

“…Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. …”

Integration of Morphoscanner with MDAnalysis, NGLview and Networkx. by Federico Fontana (533670)

datasheet1_ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed.xlsx by Paola Turina (10431428)

presentation1_ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed.pdf by Paola Turina (10431428)

VinaLigGen: a method to generate LigPlots and retrieval of hydrogen and hydrophobic interactions from protein-ligand complexes by Raghvendra Agrawal (17135479)

BrainPepPass: all scripts by Ewerton de Oliveira (16033631)

Core data by Baoqiang Chen (21099509)

“…The first convolutional layer uses a 2D convolution with 8 filters, each with a size 1 × 8, and a stride of 1, followed by a ReLU activation function. …”

Code by Baoqiang Chen (21099509)

“…The first convolutional layer uses a 2D convolution with 8 filters, each with a size 1 × 8, and a stride of 1, followed by a ReLU activation function. …”

Known compounds and new lessons: structural and electronic basis of flavonoid-based bioactivities by Rohan J. Meshram (6563189)

“…Abbreviations2′HFN-2′</p><p>hydroxy flavonone</p>2D<p>2 dimension</p>3D<p>3 dimension</p>3H7MF<p>3-hydroxy-7-methoxy flavone</p>4′HFN-4′<p>hydroxy flavonone</p>4′MF- 4′<p>methoxy flavone</p>7HFN<p>7-hydroxy flavonone</p>CHARMM<p>Chemistry at Harvard Macromolecular Mechanics</p>COX<p>cyclooxygenase</p>COX-1<p>cyclooxygenase-1</p>COX-2<p>cyclooxygenase-2</p>DM<p>dipole moment</p>DPPH- 2, 2<p>diphenyl-1-picryl hydrazine</p>EA<p>electron affinities</p>EGFR<p>epidermal growth factor receptor</p>E-HOMO<p>Highest occupied molecular orbital energy</p>E-LUMO<p>Lowest unoccupied molecular orbital energy</p>EPA<p>eicosapentaenoic acid</p>FROG2<p>FRee Online druG conformation generation</p>GA<p>Genetic Algorithm</p>GROMACS<p>GROningen MAchine for Chemical Simulations</p>HOMO<p>Highest occupied molecular orbital</p>IP<p>Ionization potential</p>LOMO<p>Lowest unoccupied molecular orbital</p>MD<p>Molecular dynamics</p>MO<p>Molecular orbital</p>NAMD<p>Nanoscale Molecular Dynamics</p>NSAIDs<p>Non-Steroidal Anti Inflammatory Drugs</p>Ns<p>nanoseconds</p>NVE<p>Ensemble-constant-energy, constant-volume, Constant particle ensemble</p>PDB-ID<p>Protein Data Bank Identifier</p>PME<p>Particle Mesh Ewald</p>PyRX<p>Python Prescription</p>RMSD<p>Root-Mean-Square Deviation</p>RMSF<p>Root-Mean-Square Fluctuation</p>RLS<p>reactive lipid species</p>ROS<p>Reactive Oxygen Species</p>SASA<p>solvent accessible surface area</p>SMILES<p>simplified molecular-input line-entry system</p>SOR<p>superoxide anion radical</p>UFF<p>Universal force field</p>VEGF<p>vascular endothelial growth factor</p>VEGFR<p>vascular endothelial growth factor receptor</p>VMD<p>Visual molecular dynamics</p><p></p> <p>hydroxy flavonone</p> <p>2 dimension</p> <p>3 dimension</p> <p>3-hydroxy-7-methoxy flavone</p> <p>hydroxy flavonone</p> <p>methoxy flavone</p> <p>7-hydroxy flavonone</p> <p>Chemistry at Harvard Macromolecular Mechanics</p> <p>cyclooxygenase</p> <p>cyclooxygenase-1</p> <p>cyclooxygenase-2</p> <p>dipole moment</p> <p>diphenyl-1-picryl hydrazine</p> <p>electron affinities</p> <p>epidermal growth factor receptor</p> <p>Highest occupied molecular orbital energy</p> <p>Lowest unoccupied molecular orbital energy</p> <p>eicosapentaenoic acid</p> <p>FRee Online druG conformation generation</p> <p>Genetic Algorithm</p> <p>GROningen MAchine for Chemical Simulations</p> <p>Highest occupied molecular orbital</p> <p>Ionization potential</p> <p>Lowest unoccupied molecular orbital</p> <p>Molecular dynamics</p> <p>Molecular orbital</p> <p>Nanoscale Molecular Dynamics</p> <p>Non-Steroidal Anti Inflammatory Drugs</p> <p>nanoseconds</p> <p>Ensemble-constant-energy, constant-volume, Constant particle ensemble</p> <p>Protein Data Bank Identifier</p> <p>Particle Mesh Ewald</p> <p>Python Prescription</p> <p>Root-Mean-Square Deviation</p> <p>Root-Mean-Square Fluctuation</p> <p>reactive lipid species</p> <p>Reactive Oxygen Species</p> <p>solvent accessible surface area</p> <p>simplified molecular-input line-entry system</p> <p>superoxide anion radical</p> <p>Universal force field</p> <p>vascular endothelial growth factor</p> <p>vascular endothelial growth factor receptor</p> <p>Visual molecular dynamics</p> <p>Communicated by Ramaswamy H. …”

Expression vs genomics for predicting dependencies by Broad DepMap (5514062)

“…In python, these files can be read in with:</p><p dir="ltr"><br></p><p dir="ltr"> import pandas as pd</p><p dir="ltr"> import h5py</p><p dir="ltr"><br></p><p dir="ltr"> def read_hdf5(filename):</p><p dir="ltr"> src = h5py.File(filename, 'r')</p><p dir="ltr"> try:</p><p dir="ltr"> dim_0 = [x.decode('utf8') for x in src['dim_0']]</p><p dir="ltr"> dim_1 = [x.decode('utf8') for x in src['dim_1']]</p><p dir="ltr"> data = np.array(src['data'])</p><p dir="ltr"><br></p><p dir="ltr"> return pd.DataFrame(index=dim_0, columns=dim_1, data=data)</p><p dir="ltr"> finally:</p><p dir="ltr"> src.close()</p><p dir="ltr"><br></p><p>##################################################################</p><p dir="ltr">Files (not every dataset will have every type of file listed below):</p><p>##################################################################</p><p dir="ltr"><br></p><p dir="ltr">AllFeaturePredictions.hdf5: Matrix of cell lines by perturbations, with values indicating the predicted viability using a model with all feature types.…”