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Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

Science (New York, N.Y.)
We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-...

Perturbation Theory Machine Learning Models: Theory, Regulatory Issues, and Applications to Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology.

Current topics in medicinal chemistry
Machine Learning (ML) models are very useful to predict physicochemical properties of small organic molecules, proteins, proteomes, and complex systems. These methods may be useful to reduce the cost of research in terms of materials resources, time,...

Semantic closure demonstrated by the evolution of a universal constructor architecture in an artificial chemistry.

Journal of the Royal Society, Interface
We present a novel stringmol-based artificial chemistry system modelled on the universal constructor architecture (UCA) first explored by von Neumann. In a UCA, machines interact with an abstract description of themselves to replicate by copying the ...

POTENTIOMETRIC STUDY OF Pd(II) COMPLEXES OF SOME FLAVONOIDS IN WATER-METHANOL-1,4-DIOXANE-ACETONITRILE (MDM) MIXTURE.

Acta poloniae pharmaceutica
The potentiometric method was used to determine the protonation (dissociation) constants for morin, rutin and chrysin and the composition and formation constants of the Pd(II)-flavonoid complexes in the water/methanol/acetonitrile/1,4-dioxane mixture...

Development of an ANN optimized mucoadhesive buccal tablet containing flurbiprofen and lidocaine for dental pain.

Acta pharmaceutica (Zagreb, Croatia)
A novel mucoadhesive buccal tablet containing flurbiprofen (FLB) and lidocaine HCl (LID) was prepared to relieve dental pain. Tablet formulations (F1-F9) were prepared using variable quantities of mucoadhesive agents, hydroxypropyl methyl cellulose (...

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space.

The journal of physical chemistry letters
Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and ap...

Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

IEEE/ACM transactions on computational biology and bioinformatics
Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can furt...

A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors.

SAR and QSAR in environmental research
Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two qu...

Artificial neural networks for dihedral angles prediction in enzyme loops: a novel approach.

International journal of bioinformatics research and applications
Structure prediction of proteins is considered a limiting step and determining factor in drug development and in the introduction of new therapies. Since the 3D structures of proteins determine their functionalities, prediction of dihedral angles rem...

Prediction of bioactive peptides using artificial neural networks.

Methods in molecular biology (Clifton, N.J.)
Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerf...