Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Molecular modeling studies on HIV-1 Reverse Transcriptase (RT) and Heat shock protein (Hsp) 90 as a potential anti-HIV-1 target.

Masters Degree. University of KwaZulu-Natal, Durban.Human immunodeficiency virus (HIV) infection is the leading cause of death globally. This dissertation addresses two HIV-1 target proteins namely, HIV-1 Reverse Transcriptase (RT) and Heat shock protein (Hsp) 90. More specifically for HIV-1 RT, a case study for the identification of potential inhibitors as anti-HIV agents was carried out. A more refined virtual screening (VS) approach was implemented, which was an improvement on work previously published by our group- “target-bound pharmacophore modeling approach”. This study generated a pharmacophore library based only on highly contributing amino acid residues (HCAAR), instead of arbitrary pharmacophores, most commonly used in the conventional approaches in literature. HCAAR were distinguished based on free binding energy (FBE) contributions, obtained using calculation from molecular dynamics (MD) simulations. Previous approaches have relied on the docking score (DS) to generate energy-based pharmacophore models. However, DS are reportedly unreliable. Thus we present a model for a per-residue energy decomposition (PRED), constructed from MD simulation ensembles generating a more trustworthy pharmacophore model which can be applied in drug discovery workflow. This approach was employed in screening for potential HIV-1 RT inhibitors using the pharmacophoric features of the compound GSK952. The complex was subjected to docking and thereafter MD simulations confirmed the stability of the system. Experimentally determined inhibitors with known HIV-RT inhibitory activity were used to validate the proposed protocol. Two potential hits ZINC46849657 and ZINC54359621 showed a significant potential with regards to FBE. Reported results obtained from this work confirm that this new approach is favourable to the future of drug design process. Hsp90 was recently discovered to play a vital role in HIV-1 replication. Thus has emerged, as a promising target for anti-HIV-1 drugs. The molecular mechanism of Hsp90 is poorly understood, thus the second study was aimed to address this issue and provide a clear insight to the inhibition mechanism of Hsp90. Reasonable continuous MD simulations were employed for both unbound and bound Hsp90 conformations, to understand the dimerization and inhibition mechanisms. Results demonstrated that coumermycin A1 (C-A1), a newly discovered Hsp90 inhibitor, binds at the CTD dimer of Hsp90 and lead to a significant separation between orthogonally opposed residues, such as Arg591.B, Lys594.A, Ser663.A, Thr653.B, Ala665.A, Thr649.B, Leu646.B and Asn669A. A Large difference in magnitudes was observed in the radius of gyration (Rg), per-residue fluctuation, root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) confirming a completely more flexible state for the unbound conformation associated with dimerization. Whereas, a less globally correlated motion in the case of the bound conformer of Hsp90 approved a reduction of the dimeric process. This undoubtedly underlines the inhibition process due to ligand binding. The detailed dynamic analyses of Hsp90 presented herein are believed to give a greater insight and understanding to the function and mechanisms of inhibition of Hsp90. The report on the inhibitor-binding mode would also be of great assistance in the design of prospective inhibitors against Hsp90 as potential HIV target

Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties

[Abstract] Using computational algorithms to design tailored drug cocktails for highly active antiretroviral therapy (HAART) on specific populations is a goal of major importance for both pharmaceutical industry and public health policy institutions. New combinations of compounds need to be predicted in order to design HAART cocktails. On the one hand, there are the biomolecular factors related to the drugs in the cocktail (experimental measure, chemical structure, drug target, assay organisms, etc.); on the other hand, there are the socioeconomic factors of the specific population (income inequalities, employment levels, fiscal pressure, education, migration, population structure, etc.) to study the relationship between the socioeconomic status and the disease. In this context, machine learning algorithms, able to seek models for problems with multi-source data, have to be used. In this work, the first artificial neural network (ANN) model is proposed for the prediction of HAART cocktails, to halt AIDS on epidemic networks of U.S. counties using information indices that codify both biomolecular and several socioeconomic factors. The data was obtained from at least three major sources. The first dataset included assays of anti-HIV chemical compounds released to ChEMBL. The second dataset is the AIDSVu database of Emory University. AIDSVu compiled AIDS prevalence for >2300 U.S. counties. The third data set included socioeconomic data from the U.S. Census Bureau. Three scales or levels were employed to group the counties according to the location or population structure codes: state, rural urban continuum code (RUCC) and urban influence code (UIC). An analysis of >130,000 pairs (network links) was performed, corresponding to AIDS prevalence in 2310 counties in U.S. vs. drug cocktails made up of combinations of ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4856 protocols, and 10 possible experimental measures. The best model found with the original data was a linear neural network (LNN) with AUROC > 0.80 and accuracy, specificity, and sensitivity ≈ 77% in training and external validation series. The change of the spatial and population structure scale (State, UIC, or RUCC codes) does not affect the quality of the model. Unbalance was detected in all the models found comparing positive/negative cases and linear/non-linear model accuracy ratios. Using synthetic minority over-sampling technique (SMOTE), data pre-processing and machine-learning algorithms implemented into the WEKA software, more balanced models were found. In particular, a multilayer perceptron (MLP) with AUROC = 97.4% and precision, recall, and F-measure >90% was found

Molecular modeling studies on HIV-1 inhibitors and their potential use as anticancer agents.

M. Pharm. University of KwaZulu-Natal, Durban 2014.Acquired Immunodeficiency Syndrome (AIDS), currently regarded as one of the deadliest diseases, is a disease of the human immune system caused by the Human Immunodeficiency Virus (HIV). This dissertation addresses two classes of HIV-1 inhibitors: (i) integrase and (ii) protease inhibitors. With the first class, a 2D-QSAR study was carried out on compounds from a variety of structural classes; 40 diketo acid and carboxamide derivatives; possessing integrase inhibitory activity. This study investigated the relationship between molecular properties and HIV-1 integrase inhibitor activities and established accurate QSAR predictive model using the Genetic Function Algorithm (GFA) statistical model. The logarithmic inverse values of IC50 (μM) and physicochemical parameters represent the dependent variable and independent variable, respectively. Results demonstrated that the radius of gyration, Zagreb index, Wiener index and minimized energy are statistically significant with the correlation coefficient value of 0.820 and play an important role in HIV-1 integrase inhibition. With the second class, the binding affinities of some FDA-approved HIV-1 protease inhibitors, which were reported to possess anticancer activities, were estimated. The findings proposed here may alter perceptions about how NFV binds to the human Hsp90; the protein responsible for the overexpression of HER2+ breast cancer; since it has only been reported to inhibit NSCLC and a collection of yeast strains. A human Hsp90 homologue was built due to the lack of a full X-ray crystal structure of the human Hsp90 on protein data bank. The Ramachandran plot showed the validity of the human Hsp90 homologue where 98% of all residues, including the active site residues, were in the favoured region and 99.8% were in the allowed region. The NTD active acid residues were found to be Leu43, Asn46, Lys53, Ile91, Asp97, Met93, Asn101, Ser108, Gly109, Phe133 and Thr179. The obtained active site residues for the human Hsp90 homologue CTD were Gln523, Val534, Ser535, Lys538, Thr595, Tyr596, Gly597, Trp598 and Met602. The system stability and overall convergence of simulations were evaluated. The RMSD of all nine PIs did not exceed 2Å and the system stabilised after 1000 ps and 1800 ps MD simulation at the NTD and CTD, respectively. The fluctuations of potential energies at the NTD were <2000 kcal/mol for 5 ns of MD simulation and CTD show that the fluctuations of the potential energy to be ≤8000 kcal/mol. The free binding energy of NFV was -83.03 kcal/mol at the NTD and -39.3 kcal/mol at the CTD. This value shows a significant difference (~43.73 kcal/mol) between the interaction energy at the NTD and CTD. Energy decomposition analysis at the NTD and CTD show that these two active sites have major energy contributions from their respective active site residues. This study is of great importance to medicine as it predicts the biological activity of some potent HIV-1 IN and investigates the potential use of the current HIV-1 PR drugs as anticancer agents

Comparative fitness analysis of proteolytic cleavage site vaccine variants in simian immunodeficiency virus

Over the past few decades, the human immunodeficiency virus and its progression to acquired immunodeficiency syndrome has become one of the most prominent global health issues. As the number of infected persons continues to grow, it is increasingly important to develop a protective vaccine to stop HIV transmission, and a cure for those already infected. Although current combination antiretroviral therapy can help patients maintain undetectable levels of the virus throughout their bodies, once the treatment is stopped, the virus will rebound. In this project, the effects of a vaccine therapy that targets the protease cleavage sites (PCS) of the HIV protease were evaluated in 16 Cynomolgus macaques. Preliminary results of the study show that in the vaccine group (n=11), a disruption to one or more of the HIV protease cleavage sites leads to a better maintenance of CD4+ T cells versus that in the control group (n=5). Furthermore, a correlation between the percentage of PCS mutations and viral load was also observed. Upon closer analysis, it was determined that the most common sites of mutation occur at PCS2 and PCS12. To assess the impact of these PCS mutations on viral fitness, we used site directed mutagenesis to introduce single amino acid mutations into a fully infectious SIV clone (SIVmac239). Ongoing studies include producing virus stocks of the SIVmac239 mutants (with multiple PCS mutations) and evaluating the viral fitness of the SIVmac239 clones in cell lines using growth competition assays. The data from this study and future studies will help provide information in the areas of vaccine and therapy development for HIV

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery

Modelos multi-escala de inteligencia artificial para diseño quimio-informático y fármaco-epidemiológico de terapias anti-VIH en Condados de Estados Unidos

[Resumen]Los métodos que relacionan la estructura química con la actividad biológica se conocen como “relaciones cuantitativas estructura-actividad” (en adelante QSAR). Es fundamental entender y cuantificar la relación entre la estructura y la actividad biológica de los potenciales fármacos para realizar su estudio eficiente. Este tipo de estudio consiste en correlacionar, por medio de descriptores moleculares, distintas propiedades químicas o fisicoquímicas de las moléculas en cuestión con valores de actividad biológica. Actualmente, el desarrollo de medicamentos más seguros y efectivos en el tratamiento de enfermedades como el SIDA es un objetivo que requiere del esfuerzo de un elevado número de especialistas en diferentes campos de la Ciencia, y donde el azar ha tenido un gran protagonismo. Sin embargo, parece razonable pensar que nunca se obtendrán medicamentos eficaces y seguros con sólo acudir al azar. Para ser más eficientes en el desarrollo de nuevos fármacos, la investigación en el tratamiento de las enfermedades requiere poseer mecanismos predictivos de algunas actividades. Los modelos basados en “redes de neuronas artificiales” (en adelante RRNNAA) son un ejemplo de modelos teóricos de predicción, ampliamente utilizados en muchas áreas de la Ciencia, como medicina, química, bioquímica…, así como también en el desarrollo de medicamentos. En esto último, son muy útiles para la predicción de propiedades de los potenciales fármacos. Las RRNNAA se aproximan a la forma de operar que usa el cerebro humano, con habilidad para abordar con éxito los datos, las informaciones y los conocimientos naturales, o del mundo real, que están afectados por lo que se conoce como la “maldición de la cuádruple I”, por ser datos: inciertos, inconsistentes, incompletos e imprecisos. Esta particularidad hace que sean difíciles de gestionar adecuadamente por las técnicas computacionales convencionales, haciendo precisa la utilización de técnicas de Inteligencia Artificial, como son las ya citadas RRNNAA. La mayor ventaja de estos modelos inteligentes de predicción es que permiten evitar costes innecesarios producidos por desarrollos de nuevos compuestos con potencialidad terapéutica que resultarán estériles.Por lo tanto, el objetivo principal de la tesis aquí presentada es el desarrollo, con técnicas de inteligencia artificial, de una metodología “quimioinformática multi-escala” que permita relacionar cuantitativamente datos químicos y pre-clínicos con datos epidemiológicos, para llevar a cabo predicciones “fármaco-epidemiológicas”, teniendo en cuenta la imposibilidad práctica y legal de obtener datos experimentales, en la fase IV del proceso de desarrollo de nuevos compuestos[Resumo]Os métodos que relacionan a estrutura química coa actividade biolóxica son chamados “relacións cuantitativas estrutura – actividade” (en adiante QSAR). É esencial para entender e cuantificar a relación entre a estrutura e a actividade biolóxica dos potenciais fármacos para realizar o seu estudio eficiente. Este tipo de estudo consiste en correlacionar, a través de descritores moleculares, distintas propiedades químicas ou fisicoquímicas de las moleculas en cuestión, con valores de actividade biolóxica. Actualmente, o desenvolvemento de medicamentos máis seguros e efectivos no tratamento de enfermidades como o SIDA é un obxectivo que require do esforzo de un gran número de especialistas en diferentes campos da ciencia, e onde o azar tivo un gran protagonismo. Nembergantes, parece razoable pensar que nunca se obterían medicamentos eficaces e seguros con só acudir ao azar. Para ser máis eficaces no desenvolvemento de novos farmacos, a investigación para o tratamento de enfermidades require mecanismos preditivos de algunhas actividades. Os modelos baseados en redes neurais artificiais (en adiante RRNNAA) son un exemplo de modelos teóricos de predición amplamente utilizado en moitas áreas da ciencia, como medicina, química, bioquímica..., así como tamén no desenvolvemento de medicamentos. Nesto último, son moi útiles para a predición de propiedades dos potenciais medicamentos. As RRNNAA achegánse ao xeito de funcionar do cerebro humano, coa capacidade para abordar con éxito los datos, las informaciones y los conocimientos naturales, o del mundo real, que están afectados polo que se coñece como a “maldición da cuadrúple I”, por ser dados: incertos, inconsistentes, incompletos e imprecisos. Esta particularidade fai que sexan díficiles de xestionar axeitadamente coas técnicas computacionais convencionais, facendo preciso o uso de técnicas de Intelixencia Artificial, como son as xa citadas RRNNAA. A maior vantaxe destes modelos preditivos intelixentes é que permiten evitar custos innecesarios producidos polos desenvolvementos de novos compostos con potencial terapéutico que resultaran esteriles. Polo tanto o obxectivo principal da tese aquí presentada é o desenvolvemento, con tecnicas de intelixencia artificial dunha metodoloxía “quimioinformática multi-escala” que permita relacionar cuantitativamente datos químicos e pre-clínicos con datos epidemiolóxicos, para levar a cabo predicións fármaco-epidemiolóxicas, tendo en conta a imposibilidade práctica e legal de obter datos experimentais na fase IV do proceso de desenvolvemento de novos compostos.[Abstract]The methods relating chemical structure to biological activity are called “Quantitative Structure Activity Relationships” (QSAR). It is essential to understand and quantify the relationships between the structure and biological activity of potential drugs to develop an efficient study on them. This kind of study consists of the correlation of the molecular descriptors based on several chemical or physicochemical properties with biological activity. Currently, the development of safer and more effective drugs in the treatment of diseases such as AIDS is a goal that requires a joint effort of a large number of specialists from different fields of science, and where chance also has a major role. However, it seems reasonable that no effective and safe drugs will be obtained based on chance only. To be more efficient in developing new drugs, the research for the treatment of diseases requires predictive mechanisms of some biological activities. The models based on "Artificial Neural Networks" (ANNs) are an example of theoretical prediction models, widely used in many areas of science such as Medicine, Chemistry, Biochemistry, etc. as well as in Drug Development. In the latter, they are very useful for predicting properties of potential drugs. ANNs approach the modus operandi used by the human brain, being able to successfully manage data, information and natural knowledge, or from the real world, which are affected by the so-called "curse of the fourfold I", dealing with information which is uncertain, inconsistent, incomplete and inaccurate. This feature makes it difficult to properly manage by conventional computational techniques, making the use of Artificial Intelligence (AI) techniques necessary, such as the above-mentioned ANNs. The most important advantage of these intelligent prediction models is the fact that they avoid unnecessary production costs associated with the development of new compounds with therapeutic potential which proved to be inactive. Therefore, the main objective of the thesis is the development of a chemoinformatics multi-scale methodology using artificial intelligence techniques to quantitatively relate chemical and pre-clinical data with epidemiological data, with the aim of performing "drug - epidemiological" predictions, taking into account the practical and legal impossibility of obtaining experimental data in Phase IV of the development process of new compounds