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Deep learning for molecular design—a review of the state of the art
Several important high level themes emerge, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training.
Deep learning for molecular generation and optimization - a review of the state of the art
- D. Elton, Zois Boukouvalas, M. Fuge, Peter W. Chung
- Computer ScienceMolecular Systems Design & Engineering
- 11 March 2019
A recent groundswell of work which uses deep learning techniques to generate and optimize molecules and how these techniques improve the quality of existing molecules is reviewed.
Applying machine learning techniques to predict the properties of energetic materials
- D. Elton, Zois Boukouvalas, Mark S Butrico, M. Fuge, Peter W. Chung
- Computer Science, ChemistryScientific Reports
- 15 January 2018
This work presents a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints - and concludes that the best featurizing was sum over bond counting, and the best model was kernel ridge regression.
Machine Learning of Energetic Material Properties
It is determined that even when using a small training set, non-linear regression methods may create models within a useful error tolerance for screening of materials.
A New Riemannian Averaged Fixed-Point Algorithm for MGGD Parameter Estimation
- Zois Boukouvalas, S. Said, L. Bombrun, Y. Berthoumieu, T. Adalı
- Mathematics, Computer ScienceIEEE Signal Processing Letters
- 15 September 2015
This work proposes a new FP algorithm, Riemannian averaged FP (RA-FP), which can effectively estimate the scatter matrix for any value of the shape parameter and provides significantly improved performance over existing FP and method-of-moments algorithms for the estimation of the scattering matrix.
Sparsity and Independence: Balancing Two Objectives in Optimization for Source Separation with Application to fMRI Analysis
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
The authors thank Dr. Kenton White, Chief Scientist at Advanced Symbolics Inc, for providing the initial Twitter dataset, which was used for the design of the TSP.
Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization
- Zois Boukouvalas, Y. Levin-Schwartz, Rami Mowakeaa, Gengshen Fu, T. Adalı
- Computer ScienceIEEE Statistical Signal Processing Workshop (SSP)
- 1 June 2018
This work proposes a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources.
An efficient multivariate generalized Gaussian distribution estimator: Application to IVA
- Zois Boukouvalas, Gengshen Fu, T. Adalı
- Computer Science49th Annual Conference on Information Sciences…
- 18 March 2015
This paper proposes an efficient estimation technique based on the Fisher scoring (FS) and demonstrates its successful application to IVA, and quantifies the performance of MGGD parameter estimation using FS and proves the effectiveness of the new IVA algorithm using simulations.
Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials
- Zois Boukouvalas, Monica Puerto, D. Elton, Peter W. Chung, M. Fuge
- Computer Science28th European Signal Processing Conference…
- 24 January 2021
This work proposes a data fusion framework that uses Independent Vector Analysis to uncover underlying complementary information contained in different molecular featurization methods and generates a low dimensional set of features—molecular signatures—that can be used for the prediction of molecular properties and for knowledge discovery.