Document text
Principal Investigator: Negin Forouzesh
Organization: CALIFORNIA STATE UNIVERSITY LOS ANGELES
Fiscal Year: 2024
Award: $24,632
Funding agency: National Institute of General Medical Sciences
Project Summary/Abstract
High performance computing (HPC) has become an indispensable resource for scientific research in diverse
fields including deep learning, bioinformatics, and molecular simulation. In the parent award (Grant No.:
1R16GM146633), Dr. Negin Forouzesh (PI) proposed to design, develop, and test a Physics-Guided Neural
Network (PGNN) model for improving the accuracy of protein-ligand binding free energy using implicit solvent
models. Recent results on more than 300 protein-ligand complexes demonstrate that the proposed PGNN model
can successfully improve the “accuracy” of the pure physics-based model. In addition, the “interpretability” and
“transferability” of the model have been boosted compared to the purely data-driven model. In this proposal, the
PI requests the purchase of a workstation to (1) run molecular dynamics (MD) simulations, (2) train deep learning
models, and (3) run a massively parallel implementation of an optimization algorithm. The requested storage is
for saving protein-ligand structures, MD trajectories, source codes, student theses, and manuscripts. The new
equipment will enable the student assistants to run HPC simulations efficiently and will improve the quality and
extent of research in the Biomolecular area at Cal State LA.
Terms: <Acceleration><Adherence><Administrative Supplement><Affinity><Algorithms><Area><Benchmarking><Best Practice Analysis><Binding><Binding Proteins><Bio-Informatics><Bioinformatics><Biological Function><Biological Process><Cell Body><Cell Communication and Signaling><Cell Signaling><Cells><Characteristics><Chemicals><Communicable Diseases><Complex><Computer Assisted><Computer Models><Computer Simulation><Computer based Simulation><Computerized Models><Computing Methodologies><Connectionist Models><Coupling><DNA Replication><DNA Synthesis><DNA biosynthesis><Data><Data Bases><Data Set><Databases><Drug Design><Ensure><Equipment><Free Energy><G-Proteins><GTP-Binding Proteins><GTP-Regulatory Proteins><Gene Expression><Goals><Grant><Guanine Nucleotide Coupling Protein><Guanine Nucleotide Regulatory Proteins><Health><High Performance Computing><Human Figure><Human body><Hybrids><Hydrogen Oxide><Infectious Disease Pathway><Infectious Diseases><Infectious Disorder><Intracellular Communication and Signaling><Ligand Binding Protein><Ligand Binding Protein Gene><Ligands><Machine Learning><Manuscripts><Measures><Medicine><Methods><Modeling><Modernization><Molecular><Molecular Dynamics Simulation><Molecular Interaction><Neural Network Models><Neural Network Simulation><Outcomes Research><Perceptrons><Performance><Physics><Play><Preparation><Protein Binding><Protein Conformation><Proteins><Protocol><Protocols documentation><Research><Research Resources><Resources><Running><Sampling><Side><Signal Transduction><Signal Transduction Pathway><Signal Transduction Systems><Signaling><Solvents><Source><Source Code><Structure><Students><System><Testing><Training><Water><Weight><benchmark><biological signal transduction><bound protein><computational methodology><computational methods><computational modeling><computational models><computational simulation><computer aided><computer based method><computer based models><computer methods><computerized modeling><computerized simulation><computing method><data base><data cleaning><data cleansing><data-driven model><deep learning><deep learning based model><deep learning method><deep learning model><deep learning strategy><design><designing><drug discovery><experiment><experimental research><experimental study><experiments><flexibility><flexible><foot><graph attention network><graph convolutional network><graph neural network><high-end computing><improved><lab assignment><lab experiment><laboratory activity><laboratory assignment><laboratory exercise><laboratory experiment><loss of function><machine based learning><machine learning based model><machine learning model><molecular dynamics><molecular recognition><neglect><neural network><novel><parent award><parent project><physical model><preparations><prevent><preventing><programs><protein folding><protonation><prototype><response><simulation><small molecule><structural biology><usability><web services><web-based service><weights>