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The development of new molecules for the treatment of calmodulin related cardiovascular or neurodegenerative diseases is an interesting goal. In this work, we introduce a novel strategy with four main steps: (1) chemical synthesis of target molecules, (2) Förster Resonance Energy Transfer (FRET) biosensor development and in vitro biological assay of new derivatives, (3) Cheminformatics models development and in vivo activity prediction, and (4) Docking studies. This strategy is illustrated with a case study. Firstly, a series of 4-substituted Riluzole derivatives 1-3 were synthetized through a strategy that involves the construction of the 4-bromoriluzole framework and its further functionalization via palladium catalysis or organolithium chemistry. Next, a FRET biosensor for monitoring Ca 2+ -dependent CaM-ligands interactions has been developed and used for the in vitro assay of Riluzole derivatives. In particular, the best inhibition (80%) was observed for 4-methoxyphenylriluzole 2b. Besides, we trained and validated a new Networks Invariant, Information Fusion, Perturbation Theory, and Machine Learning (NIFPTML) model for predicting probability profiles of in vivo biological activity parameters in different regions of the brain. Next, we used this model to predict the in vivo activity of the compounds experimentally studied in vitro. Last, docking study conducted on Riluzole and its derivatives has provided valuable insights into their binding conformations with the target protein, involving calmodulin and the SK4 channel. This new combined strategy may be useful to reduce assay costs (animals, materials, time, and human resources) in the drug discovery process of calmodulin inhibitors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R 2  = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products., (© 2024. The Author(s).)

In this work, the SOFT.PTML tool has been used to pre-process a ChEMBL dataset of pre-clinical assays of antileishmanial compound candidates. A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81-98%) in training and validation series. The use of this software has been illustrated with a practical case study focused on a series of 28 derivatives of 2-acylpyrroles 5a , b , obtained through a Pd(II)-catalyzed C-H radical acylation of pyrroles. Their in vitro leishmanicidal activity against visceral ( L. donovani ) and cutaneous ( L. amazonensis ) leishmaniasis was evaluated finding that compounds 5bc (IC 50 = 30.87 μM, SI > 10.17) and 5bd (IC 50 = 16.87 μM, SI > 10.67) were approximately 6-fold more selective than the drug of reference (miltefosine) in in vitro assays against L. amazonensis promastigotes. In addition, most of the compounds showed low cytotoxicity, CC 50 > 100 μg/mL in J774 cells. Interestingly, the IFPMTL-LOGR model predicts correctly the relative biological activity of these series of acylpyrroles. A computational high-throughput screening (cHTS) study of 2-acylpyrroles 5a , b has been performed calculating >20,700 activity scores vs a large space of 647 assays involving multiple Leishmania species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. The present work also points to 2-acylpyrroles as new lead compounds worthy of further optimization as antileishmanial hits.

The use of MW allows the efficient palladium(II)-catalysed C-3 acylation of thiophenes with aldehydes via C(sp 2 )-H activation for the synthesis of (cyclo)alkyl/aryl thienyl ketones (43 examples). Compared to standard thermal conditions, the use of MW reduces the reaction time (15 to 30 min vs . 1 to 3 hours), leading to improved yields of the ketones (up to 92%). The control of positional selectivity is achieved by 2-pyridinyl and 2-pyrimidyl ortho -directing groups at C-2 of the thiophene scaffold. To show the synthetic applicability, selected ketones were subjected to further transformations, including intramolecular reactions to directly embed the directing group in the core structure of the new molecule.

Highly substituted coumarins, privileged and versatile scaffolds for bioactive natural products and fluorescence imaging, are obtained via a Pd(II)-catalyzed direct C-H alkenylation reaction (Fujiwara-Moritani reaction), which has emerged as a powerful tool for the construction and functionalization of heterocyclic compounds because of its chemical versatility and its environmental advantages. Thus, a selective 6- endo cyclization led to 4-substituted coumarins in moderate yields. Selected examples have been further functionalized in C3 through a second intermolecular C-H alkenylation reaction to give coumarin-acrylate hybrids, whose fluorescence spectra have been measured., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

The development of new molecules for the treatment of leishmaniasis is, a neglected parasitic disease, is urgent as current anti-leishmanial therapeutics are hampered by drug toxicity and resistance. The pyrrolo[1,2-b]isoquinoline core was selected as starting point, and palladium-catalyzed Heck-initiated cascade reactions were developed for the synthesis of a series of C-10 substituted derivatives. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated. The best activity was found, in general, for the 10-arylmethyl substituted pyrroloisoquinolines. In particular, 2ad (IC 50  = 3.30 μM, SI > 77.01) and 2bb (IC 50  = 3.93 μM, SI > 58.77) were approximately 10-fold more potent and selective than the drug of reference (miltefosine), against L. amazonensis on in vitro promastigote assays, while 2ae was the more active compound in the in vitro amastigote assays (IC 50  = 33.59 μM, SI > 8.93). Notably, almost all compounds showed low cytotoxicity, CC 50  > 100 μg/mL in J774 cells, highest tested dose. In addition, we have developed the first Perturbation Theory Machine Learning (PTML) algorithm able to predict simultaneously multiple biological activity parameters (IC 50 , K i , etc.) vs. any Leishmania species and target protein, with high values of specificity (>98%) and sensitivity (>90%) in both training and validation series. Therefore, this model may be useful to reduce time and assay costs (material and human resources) in the drug discovery process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

The developing of antibacterial resistance is becoming in crisis. In this sense, natural products play a fundamental role in the discovery of antibacterial agents with diverse mechanisms of action. Phytochemical investigation of Cissus incisa leaves led to isolation and characterization of the ceramides mixture (1): (8E)-2-(tritriacont-9-enoyl amino)-1,3,4-octadecanetriol-8-ene (1-I); (8E)-2-(2',3'-dihydroxyoctacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-II); (8E)-2-(2'-hydroxyheptacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-III); and (8E)-2-(-2'-hydroxynonacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-IV). Until now, this is the first report of the ceramides (1-I), (1-II), and (1-IV). The structures were elucidated using NMR and mass spectrometry analyses. Antibacterial activity of ceramides (1) and acetylated derivates (2) was evaluated against nine multidrug-resistant bacteria by Microdilution method. (1) showed the best results against Gram-negatives, mainly against carbapenems-resistant Acinetobacter baumannii with MIC = 50 μg/mL. Structure-activity analysis and molecular docking revealed interactions between plant ceramides with membrane proteins, and enzymes associated with biological membranes of Gram-negative bacteria, through hydrogen bonding of functional groups. Vesicular contents release assay showed the capacity of (1) to disturb membrane permeability detected by an increase of fluorescence probe over time. The membrane disruption is not caused for ceramides lytic action on cell membranes, according in vitro hemolyticactivity results. Combining SAR analysis, bioinformatics and biophysical techniques, and also experimental tests, it was possible to explain the antibacterial action of these natural ceramides., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The use of earth-abundant first-row transition metals, such as cobalt, in C-H activation reactions for the construction and functionalization of a wide variety of structures has become a central topic in synthetic chemistry over the last few years. In this context, the emergence of cobalt catalysts bearing pentamethylcyclopentadienyl ligands (Cp*) has had a major impact on the development of synthetic methodologies. Cp*Co(III) complexes have been proven to possess unique reactivity compared, for example, to their Rh(III) counterparts, obtaining improved chemo- or regioselectivities, as well as yielding new reactivities. This perspective is focused on recent advances on the alkylation and alkenylation reactions of (hetero)arenes with alkenes and alkynes under Cp*Co(III) catalysis., Competing Interests: The authors declare no competing financial interest.

The first example of Cp*Co(III)-catalyzed intramolecular hydroarylation of allyl aryl ethers using an amide directing group for the preparation of 3,3-disubstituted dihydrobenzofurans in high yields is described. The reaction of the unactivated alkene is completely selective for the formation of the quaternary center, allowing different substitution patterns on the aromatic ring and the alkene. The cyclization can also be extended to the formation of six-membered rings and to N -homoallylindoles.

Di(hetero)aryl ketones are important motifs present in natural products, pharmaceuticals or agrochemicals. In recent years, Pd(II)-catalyzed acylation of (hetero)arenes in the presence of an oxidant has emerged as a catalytic alternative to classical acylation methods, reducing the production of toxic metal waste. Different directing groups and acyl sources are being studied for this purpose, although further development is required to face mainly selectivity problems in order to be applied in the synthesis of more complex molecules. Selected recent developments and applications are covered in this review.

A protocol for the Pd(II)-catalyzed C-H alkenylation reaction of substituted N -allylanilines via an unusual 6- endo process has been developed. A density functional theory (DFT) study of the mechanistic pathway has shown that the coordination of the remote protecting group to the palladium center is determinant for the control of the regioselectivity in favor of the 6- endo process. The reaction would proceed via prior activation of the alkene. This procedure constitutes a mild and efficient method for the synthesis of 1,4-dihydroquinoline derivatives from simple and readily accessible substrates.

Aims: 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., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and assay conditions like c 0 = activity parameter ( K i , IC 50 , etc.), c 1 = target protein, c 2 = cell line, c 3 = assay organism, etc., making difficult the analysis of these databases that are placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to perform new predictive and experimental studies of selective 5-HTA2 receptor agonist, antagonist, or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this data set. We analyzed 343 738 unique compounds with 812 072 end points (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56 202/58 653 positive outcomes (sensitivity = 95.8%) and 470 230/550 401 control cases (specificity = 85.4%) in training series. The model also predicted correctly 18 732/19 549 (95.8%) of positive outcomes and 156 739/183 469 (85.4%) of cases in external validation series. To illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, namely, TCB-2, DOI, DOB, altanserin, pimavanserin, and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 of 86) experimental results for these compounds reported in ChEMBL. Moreover, [ 35 S]GTPγS binding assays were performed experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists, or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.

A convergent route to pyrrolo[1,2- b ]isoquinolines with a quaternary center at C-10 has been developed, which implies a sequential Pd(0)-catalyzed carbopalladation followed by cross-coupling reaction with boronic acids. The adequate catalytic system and experimental conditions, with and without the use of phosphane ligands, have been selected to control the chemoselectivity of the process, allowing a 6-exo-carbopalladation to generate a quaternary center and avoiding a direct Suzuki coupling. A variety of electron-rich and electron-deficient arylboronic acids can be used providing an efficient route to substituted pyrrolo[1,2- b ]isoquinolines in moderate to good yields (up to 94%, 22 examples).

Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN s ) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC 50 , etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.

The intramolecular Pd(II)-catalyzed alkenylation of aryl homoallyl ethers constitutes a mild, versatile, and efficient procedure for the synthesis of highly and diversely substituted chromanes and 2 H-chromenes. The use of p-TsOH as an additive allows more efficient reactions that could be carried out a room temperature in most cases. The procedure has a wide scope, allowing the synthesis of alkylidenechromanes and 2 H-chromenes substituted at C-2 or C-3 of the chromene moiety, thus accessing relevant flavenes and isoflavenes, and even coumarins, in high yields (59 to 91%, 32 examples).

Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all the molecules involved in a query reaction, compared to a reaction of reference. Thus, a dataset of >100 reactions has been collected for different substrates and internal electrophiles, under different reaction conditions, with a wide range of yields (0-98%). The best PTML model found using General Linear Regression (GLR) has R = 0.88 in training and R = 0.83 in external validation series for 10 000 pairs of query and reference reactions. The PTML model has a final R = 0.95 for all reactions using multiple reactions of reference. We also report a comparative study of linear versus nonlinear PTML models based on artificial neural network (ANN) algorithms. PTML-ANN models (LNN, MLP, RBF) with R ≈ 0.1-0.8 do not outperform the first PMTL model. This result confirms the validity of the linearity of the model. Next, we carried out an experimental and theoretical study of nonreported Parham reactions to illustrate the practical use of the PTML model. A 500 000-point simulation and a Hammett analysis of the reactivity space of Parham reactions are also reported.

Palladium-catalyzed dehydrogenative coupling is an efficient synthetic strategy for the construction of quinoline scaffolds, a privileged structure and prevalent motif in many natural and biologically active products, in particular in marine alkaloids. Thus, quinolines and 1,2-dihydroquinolines can be selectively obtained in moderate-to-good yields via intramolecular C-H alkenylation reactions, by choosing the reaction conditions. This methodology provides a direct method for the construction of this type of quinoline through an efficient and atom economical procedure, and constitutes significant advance over the existing procedures that require preactivated reaction partners., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

An organolithium addition-intramolecular α-amidoalkylation sequence on N -phenethylimides has been developed for the synthesis of fused tetrahydroisoquinoline systems using 1,1'-bi-2-naphthol (binol)-derived Brønsted acids. This transformation is the first in which activated benzene derivatives are used as internal nucleophiles, instead of electron-rich heteroaromatics, generating a quaternary stereocenter. Phenolic substitution on the aromatic ring of the phenethylamino moiety and the use of binol-derived N -triflylphosphoramides as catalysts are determinants to achieve reasonable levels of enantioselection, that is, up to 75% enantiomeric excess, in the α-amidoalkylation step. The procedure is complementary to the intermolecular α-amidoalkylation process, as opposite enantiomers are formed, and to the Pictet-Spengler cyclization, which allows the formation of tertiary stereocenters., Competing Interests: The authors declare no competing financial interest.

Enamides with a free NH group have been evaluated as nucleophiles in chiral Brønsted acid-catalyzed enantioselective α-amidoalkylation reactions of bicyclic hydroxylactams for the generation of quaternary stereocenters. A quantitative structure-reactivity relationship (QSRR) method has been developed to find a useful tool to rationalize the enantioselectivity in this and related processes and to orient the catalyst choice. This correlative perturbation theory (PT)-QSRR approach has been used to predict the effect of the structure of the substrate, nucleophile, and catalyst, as well as the experimental conditions, on the enantioselectivity. In this way, trends to improve the experimental results could be found without engaging in a long-term empirical investigation.

The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the effects of in-out perturbations in intra-molecular carbolithiations, drug ADME process, or self-aggregation of micelle nanoparticles of drugs or surfactants. The Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp) of these models were >90% in all cases. The first model predicts variations in the yield or enantiomeric excess due to structural variations or changes in the solvent, temperature, temperature of addition, or time of reaction. The second model predicts changes in >18 parameters of biological effects for >3000 assays of ADME properties and/or interactions between 31,723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model predicts perturbations due to changes in temperature, solvent, salt concentration, and/or structure of anions or cations in the self-aggregation of micelle nanoparticles of drugs and surfactants.

Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.

In general perturbation methods starts with a known exact solution of a problem and add "small" variation terms in order to approach to a solution for a related problem without known exact solution. Perturbation theory has been widely used in almost all areas of science. Bhor's quantum model, Heisenberg's matrix mechanincs, Feyman diagrams, and Poincare's chaos model or "butterfly effect" in complex systems are examples of perturbation theories. On the other hand, the study of Quantitative Structure-Property Relationships (QSPR) in molecular complex systems is an ideal area for the application of perturbation theory. There are several problems with exact experimental solutions (new chemical reactions, physicochemical properties, drug activity and distribution, metabolic networks, etc.) in public databases like CHEMBL. However, in all these cases, we have an even larger list of related problems without known solutions. We need to know the change in all these properties after a perturbation of initial boundary conditions. It means, when we test large sets of similar, but different, compounds and/or chemical reactions under the slightly different conditions (temperature, time, solvents, enzymes, assays, protein targets, tissues, partition systems, organisms, etc.). However, to the best of our knowledge, there is no QSPR general-purpose perturbation theory to solve this problem. In this work, firstly we review general aspects and applications of both perturbation theory and QSPR models. Secondly, we formulate a general-purpose perturbation theory for multiple-boundary QSPR problems. Last, we develop three new QSPR-Perturbation theory models. The first model classify correctly >100,000 pairs of intra-molecular carbolithiations with 75-95% of Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp). The model predicts probabilities of variations in the yield and enantiomeric excess of reactions due to at least one perturbation in boundary conditions (solvent, temperature, temperature of addition, or time of reaction). The model also account for changes in chemical structure (connectivity structure and/or chirality paterns in substrate, product, electrophile agent, organolithium, and ligand of the asymmetric catalyst). The second model classifies more than 150,000 cases with 85-100% of Ac, Sn, and Sp. The data contains experimental shifts in up to 18 different pharmacological parameters determined in >3000 assays of ADMET (Absorption, Distribution, Metabolism, Elimination, and Toxicity) properties and/or interactions between 31723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model classifies more than 260,000 cases of perturbations in the self-aggregation of drugs and surfactants to form micelles with Ac, Sn, and Sp of 94-95%. The model predicts changes in 8 physicochemical and/or thermodynamics output parameters (critic micelle concentration, aggregation number, degree of ionization, surface area, enthalpy, free energy, entropy, heat capacity) of self-aggregation due to perturbations. The perturbations refers to changes in initial temperature, solvent, salt, salt concentration, solvent, and/or structure of the anion or cation of more than 150 different drugs and surfactants. QSPR-Perturbation Theory models may be useful for multi-objective optimization of organic synthesis, physicochemical properties, biological activity, metabolism, and distribution profiles towards the design of new drugs, surfactants, asymmetric ligands for catalysts, and other materials.

In this review we summarize recent developments in inter- and intramolecular enantioselective carbolithiation reactions carried out in the presence of a chiral ligand for lithium, such as (-)-sparteine, to promote facial selection on a C=C bond. This is an attractive approach for the construction of new carbon-carbon bonds in an asymmetric fashion, with the possibility of introducing further functionalization on the molecule by trapping the reactive organolithium intermediates with electrophiles.

The Parham cyclization-intermolecular α-amidoalkylation sequence results in the facile enantioselective synthesis of 12b-substituted isoindoloisoquinolines (ee up to 95%) using BINOL-derived Brønsted acids. α-Amidoalkylation of indole occurs through the formation of a chiral conjugate base/bicyclic quaternary N-acyliminium ion pair.

The preparation of 4-substituted 2-phenyltetrahydroquinolines from N-alkenylsubstituted 2-iodoanilines via intramolecular carbolithiation reactions has been investigated. The stereochemical outcome of the carbolithiation reactions depends on the nature of organolithium employed to perform the lithium-halogen exchange, the solvent, or the use of additives, for example, TMEDA or chiral bidentated ligands such as (-)-sparteine. Thus, the 2,4-disubstituted tetrahydroquinolines are obtained with moderate diastereoselectivities (up to 77:23) and with ee up to 94% when Weinreb amide derivatives are used (R = CONMe(OMe)).

Conjugate addition reactions of sulfur-stabilized nucleophiles to the delta-lactam unit of tetrahydrobenzo[a]benzoquinolizines have been studied. The stereochemical outcome of the conjugate addition reaction depends on the nature of the substituent at the angular position, thus 2,11b-cis or 2,11b-trans diastereomers could be obtained selectively. The tandem conjugate addition-alkylation also takes place in good yields and with high diastereoselectivity. The polyfunctionalized hexahydrobenzo[a]quinolizinone systems obtained could be further elaborated toward emetine-like structures.

[reaction: see text] Stereocontrolled intramolecular alpha-amidoalkylation reactions of L-DOPA-derived succinimides have been studied. Addition of MeLi to nonracemic succinimides 9a-d yields oxoamides, which are cyclized upon treatment with Lewis or protic acids to afford (5S,10bS)-trans 11 or (5S,10bR)-cis 12pyrroloisoquinolines with variable diastereoselectivities, but with high enantiomerical purities (ee 99%).

Enantiodivergent synthesis of C-10b-substituted pyrrolo[2,1-a]isoquinolines starting from an enantiomerically pure N-phenethylnorborn-5-en-endo-2,3-dicarboxyimide 3a, with a 2-exo-hydroxy-10-bornylsulfinyl group as a chiral auxiliary, has been developed. The key transformations are derived from diastereoselective intramolecular cyclization of aryllithiums and alpha-amidoalkylation reactions, with the ethylidene bridge of the norbornene moiety dictating the stereochemical outcome in both types of reactions. Thus, the organolithium addition-intramolecular alpha-amidoalkylation sequence on imide 3a afforded stereoselectively the R configuration at C-12b, whereas the tandem Parham cyclization-intermolecular alpha-amidoalkylation reactions on the corresponding iodinated imide 3b occurred with complete control of stereoselectivity, leading to the epimer at C-12b. Subsequent reductive removal of the chiral auxiliary and retro-Diels-Alder reaction afforded (10bS)- and (10bR)-pyrroloisoquinolines 1 in high yields and optical purities (>99% ee).

[reaction: see text] Weinreb amides behave as efficient internal electrophiles in Parham-type cycliacylation reactions. Thus, aryl- and heteroaryllithiums generated by lithium-halogen exchange undergo intramolecular cyclization to give fused indolizinone systems as pyrrolo[1,2-b]isoquinolines, thieno[2,3-f]indolizinones, and pyrrolo[1,2-b]acridinones in high yields.

Sequential carbophilic addition of organolithium reagents and N-acyliminium ion cyclization of N-phenethylimides 1 affords the substituted isoquinolones 3 in high yields, with the possibility of varying the substituent at the C-1 position of the isoquinoline ring by changing the organolithium reagent. Ready access to the isoquinoline nucleus via Parham-type cyclization of imides 2 is also described. We have shown that iodinated imides 2 tolerate the metal-halogen exchange in the presence of the imide group, and the intramolecular cyclization of the so-obtained aromatic organometallic derivatives leads to the corresponding enamides 4. Both approaches have allowed the efficient preparation of various types of the isoquinoline class of alkaloids, just by changing the substitution pattern on the readily available starting imides. Thus, we have developed convenient alternative routes for the synthesis of benzo[a]quinolizidones and their 2-oxa analogs, isoindoloisoquinolones, dibenzo[a,h]quinolizidones, and thiazolo- and oxazolo[4,3-a]isoquinolones.

Efficient synthetic routes to isoquinoline alkaloids of the protoberberine and benzo[c]phenanthridine classes are reported. The key transformations are derived from the intramolecular cyclization of C-2'-functionalized N-(1,2-diarylethyl)amides or enamides via 3-arylisoquinoline derivatives. Thus, under Bischler-Napieralski reaction conditions (PCl(5), nitrile as solvent, room temperature) N-(1,2-diarylethyl)amides 12 regioselectively yielded 2,3-disubstituted 13,14-dihydroprotoberberinium salts 20, a scarcely studied oxidation state in this class of alkaloids. Subsequent reduction of the iminium bond gave the known coralydine (21a) and O-methylcorytenchirine (21b) and their 8-phenyl analogue 21c. The one-pot preparation of these dihydroprotoberberinium salts 20 is shown to proceed with cleavage of the silyl ether and immediate halogenation of the resulting hydroxyl group, followed by cyclization of the obtained N-(1,2-diarylethyl)amide 18 to a 3,4-dihydroisoquinoline derivative 19 and subsequent intramolecular in situ N-alkylation of the latter imine. Ready access to planar 8,9-dialkoxylated benzo[c]phenanthridinium salts is also described. Condensation of ketoester 23 with benzylamine in the presence of titanium(IV) chloride, followed by acetylation, afforded a mixture of naphthylamide 24 and (E)-enamide 25. Both enamides were efficiently cyclized by POCl(3). While the planar benzo[c]phenanthridinium salt 26 was directly produced from 24, the (E)-enamide 25 gave the 3-arylisoquinolinium salt 27, which was reduced and intramolecularly C-alkylated to yield the tetracyclic nucleus of these alkaloids.