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Mike Fortunato
Mike Fortunato
Verified email at novartis.com
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Year
pysimm: A python package for simulation of molecular systems
ME Fortunato, CM Colina
SoftwareX 6, 7-12, 2017
622017
Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning
ME Fortunato, CW Coley, BC Barnes, KF Jensen
Journal of chemical information and modeling 60 (7), 3398-3407, 2020
612020
Evaluating and clustering retrosynthesis pathways with learned strategy
Y Mo, Y Guan, P Verma, J Guo, ME Fortunato, Z Lu, CW Coley, KF Jensen
Chemical science 12 (4), 1469-1478, 2021
492021
Uncertainties in the predictions of thermo-physical properties of thermoplastic polymers via molecular dynamics
L Alzate-Vargas, ME Fortunato, B Haley, C Li, CM Colina, A Strachan
Modelling and Simulation in Materials Science and Engineering 26 (6), 065007, 2018
292018
Predicting in vivo compound brain penetration using multi-task graph neural networks
S Hamzic, R Lewis, S Desrayaud, C Soylu, M Fortunato, G Gerebtzoff, ...
Journal of chemical information and modeling 62 (13), 3180-3190, 2022
162022
Effects of galactosylation in immunoglobulin G from all-atom molecular dynamics simulations
ME Fortunato, CM Colina
The Journal of Physical Chemistry B 118 (33), 9844-9851, 2014
122014
Update 0.2 to “pysimm: a Python package for simulation of molecular systems”
AG Demidov, ME Fortunato, CM Colina
SoftwareX 7, 70-73, 2018
112018
Machine learned prediction of reaction template applicability for data-driven retrosynthetic predictions of energetic materials
ME Fortunato, CW Coley, BC Barnes, KF Jensen
AIP Conference Proceedings 2272 (1), 2020
82020
Update 1.1 to “pysimm: a Python package for simulation of molecular systems”,(PII: S2352711016300395)
AG Demidov, BLA Perera, ME Fortunato, S Lin, CM Colina
SoftwareX 15, 100749, 2021
62021
Pre-and Post-Processing Tools to Create and Characterize Particle-Based Composite Model Structures
ME Fortunato, J Mattson, DE Taylor, JP Larentzos, JK Brennan
US Army Research Laboratory, 2017
32017
Introduction to in silico synthesis of polymers via PySIMM [Article v1. 0]
AG Demidov, BLA Perera, ME Fortunato, S Lin, C Colina
Living Journal of Computational Molecular Science 4 (1), 1561-1561, 2022
12022
An All-Atomistic Molecular Dynamics Study to Determine the Structural Importance of Disulfide Bonds in Immunoglobulin G and Bovine Serum Albumin.
A Mathavan, A Mathavan, ME Fortunato, CM Colina
American Journal of Undergraduate Research 15 (1), 2018
12018
Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design
AD Danilack, CJ Dickson, C Soylu, M Fortunato, S Rodde, H Munkler, ...
Journal of Computer-Aided Molecular Design 38 (1), 21, 2024
2024
Computer‐aided evaluation and exploration of chemical spaces constrained by reaction pathways
I Levin, ME Fortunato, KL Tan, CW Coley
AIChE journal 69 (12), e18234, 2023
2023
Data-Driven Retrosynthetic Predictions for Energetic Materials (symp)
M Fortunato, C Coley, B Barnes, I Schweigert, A Beste, K Jensen
Bulletin of the American Physical Society 64, 2019
2019
Developing Software to Assess the Viability of Molecular Mechanics Force Fields for the Prediction of Polymer Properties
ME Fortunato
University of Florida, 2018
2018
Sorption-Relaxation Behavior in Polymers of Intrinsic Microporisity during Gas Separation from Molecular Simulations
G Kupgan, ME Fortunato, A Demidov, C Colina
2017 AIChE Annual Meeting, 2017
2017
Using Python to Standardize and Format Input to Popular Molecular Simulation Software
C Colina, ME Fortunato
2017 AIChE Annual Meeting, 2017
2017
Uncertainties of Parameters to Predictions of Polymer Properties by Molecular Simulations
L ALZATE-VARGAS, C LI, B HALEY, M FORTUNATO, C COLINA, ...
Proceedings of the American Society for Composites — Thirty-second …, 2017
2017
Immunoglobulin G: Solution Dynamics, Carbohydrate Structure, and Self-association From Atomistic and Coarse-grained Simulations
ME Fortunato
2015
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Articles 1–20