MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry

Abstract

Aim: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). Background: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). Objective: Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. Methods: We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. Results: We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. Conclusion: The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.

MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products

“…r were created. In step 2, the SMILE codes of the molecules (m qsi , m rsj ) involved in both q and r reactions (substrates, nucleophiles, catalysts, solvents, products) were entered in the MCDCalc server [ 49 ] to calculate their molecular descriptors D k (m qsi ) g and D k (m rsj ) g . In step 3, the PTOs for pairs of reactions were calculated.…”

“…First, the web tool MMDcalc was used to calculate the molecular descriptors D k (m sqi ) g and D k (m sri ) g of the molecules m sqi and m sri involved in the query and reference reactions [ 36 ]. The MMDcalc tool is an online web server available at the PTMLTool platform ( https://cptmltool.rnasa-imedir.com/ ) for public use.…”

Data-driven design of electrocatalysts: principle, progress, and perspective

“…However, the descriptor has a broader connotation, which can be the combination of multiple features or the target of the ML model. 14,[73][74][75] The denition of "descriptor" can be roughly described as an indicator of the catalyst performance, which is used as a guide to enable researchers to screen catalysts more quickly. 76 A good descriptor should be easy to obtain and have a clear physical meaning.…”

MATEO: InterMolecular α-Amidoalkylation Theoretical Enantioselectivity Optimization. Online Tool for Selection and Design of Chiral Catalysts and Products

“…First, the web tool MMDcalc was used to calculate the molecular descriptors Dk(msqi)g and Dk(msri)g of the molecules msqi and msri involved in the query and reference reactions [36]. The MMDcalc tool is an online web server available at the PTMLTool platform…”

“…In step 1, the reference dataset and reaction pairs q vs. r were created. In step 2, the SMILE codes of the molecules (mqsi, mrsj) involved in both q and r reactions (substrates, nucleophiles, catalysts, solvents, products) were entered in the MCDCalc server [49] to calculate their molecular descriptors Dk(mqsi)g and Dk(mrsj)g. In step 3, the PTOs for pairs of reactions were calculated. In step 4, the Multivariate Linear Regression (MLR) algorithm implemented in the STATISTICA [38] software was used to seek the PTML model.…”