Advancing physicochemical property predictions in computational drug discovery [PDF]
, 2022 Computer-aided drug design aims to guide the discovery of compounds with optimal pharmaceutical properties. Computational tools can evaluate large libraries of virtual molecules to help prioritize new compounds to synthesize and test. Properties such as protein-ligand binding affinity and physicochemical properties are of interest.
Bergazin, Teresa Danielle
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Molecular-Property Prediction with Sparsity [PDF]
, 2022 Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs.
Sanjar, Adilov
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SMARTS Approach to Chemical Data Mining and Physicochemical Property Prediction. [PDF]
The calculation of physicochemical and biological properties is essential in order to facilitate modern drug discovery. Chemical spaces dimensionalized by these descriptors have been used to scaffold-hop in order to discover new lead and drug-like ...
Lee, Adam C.
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Prediction of Protein Sites and Physicochemical Properties Related to Functional Specificity [PDF]
Bioengineering, 2021 Specificity Determining Positions (SDPs) are protein sites responsible for functional specificity within a family of homologous proteins. These positions are extracted from a family’s multiple sequence alignment and complement the fully conserved positions as predictors of functional sites.
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Predicting protein distance maps according to physicochemical properties
Journal of integrative bioinformatics, 2011 The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein consists in determining its three-dimensional conformation based solely on its amino acid sequence.
Gualberto Asencio-Cortés +1 more
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Hybridizing physical and data-driven prediction methods for physicochemical properties
Chemical Communications, 2020 We present a generic, highly effective approach to combine physical and data-driven prediction methods for physicochemical properties based on Bayesian machine learning and model distillation.
Lehrstuhl für Thermodynamik +3 more
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Shreddability of pizza Mozzarella cheese predicted using physicochemical properties [PDF]
Journal of Dairy Science, 2014 This study used rheological techniques such as uniaxial compression, wire cutting, and dynamic oscillatory shear to probe the physical properties of pizza Mozzarella cheeses. Predictive models were built using compositional and textural descriptors to predict cheese shreddability.
V, Banville +3 more
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Physicochemical Properties Predict Retention of Antibiotics in Water-in-Oil Droplets
Analytical Chemistry, 2023 Water-in-oil droplet microfluidics promises capacity for high-throughput single-cell antimicrobial susceptibility assays and investigation of drug resistance mechanisms. Every droplet must serve as an isolated environment with a controlled antibiotic concentration in such assays.
Artur Ruszczak +4 more
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Joint Graph-Sequence Learning for Molecular Property Prediction
, 2022 Molecular property prediction has achieved promising improvement for accelerating drug development with machine learning models. The emergence of graph neural networks especially benefits the discriminative representation learning of molecular graph data,
Uddamvathanak, R, Zheng, X, Pan, S
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Using amino acid physicochemical distance transformation for fast protein remote homology detection. [PDF]
PLoS ONE, 2012 Protein remote homology detection is one of the most important problems in bioinformatics. Discriminative methods such as support vector machines (SVM) have shown superior performance.
Bin Liu +4 more
doaj +1 more source