A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

<p>Abstract</p> <p>Background</p> <p>The current chemical space of known small molecules is estimated to exceed 10⁶⁰structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.</p> <p>Results</p> <p>The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.</p> <p>Conclusions</p> <p>Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.</p

Chatbots in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with ChatGPT

The birth of ChatGPT, a cutting-edge language model chatbot developed by OpenAI, ushered in a new era in AI, and this paper vividly showcases its innovative application within the field of drug discovery. Focused specifically on developing anti-cocaine addiction drugs, the study employs GPT-4 as a virtual guide, offering strategic and methodological insights to researchers working on generative models for drug candidates. The primary objective is to generate optimal drug-like molecules with desired properties. By leveraging the capabilities of ChatGPT, the study introduces a novel approach to the drug discovery process. This symbiotic partnership between AI and researchers transforms how drug development is approached. Chatbots become facilitators, steering researchers towards innovative methodologies and productive paths for creating effective drug candidates. This research sheds light on the collaborative synergy between human expertise and AI assistance, wherein ChatGPT's cognitive abilities enhance the design and development of potential pharmaceutical solutions. This paper not only explores the integration of advanced AI in drug discovery but also reimagines the landscape by advocating for AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation

Study of the aryl hydrocarbon receptor mediated effects through in silico modeling and in vitro bioassays

The aryl hydrocarbon receptor (AhR) is a cytoplasmatic sensor of diverse endogenous and exogenous substances. In a toxicological context, the former known as “dioxin receptor” has been investigated as a xenobiotic chemoreceptor and due to its roles in mediating carcinogenesis, endocrine disruption, among other immunological, hepatic, cardiovascular, and dermal toxicity mechanisms. The deep physiological implications of AhR in cellular proliferation, adhesion, division, differentiation, as well as in the reproductive, immunological and cardiovascular homeostasis have opened a new field of research in order to harness AhR’s pharmacological potential. Hence, AhR has become a therapeutic target of inflammatory, infectious, malignant, and immunological conditions. Toxicological and pharmacological fields could benefit from discovering novel AhR modulators to elucidate further on the chemical-biological implications of this crucial transcription factor. In this Thesis, the following objective was proposed in order to contribute to such understanding. General Objective: Evaluate diverse chemical compounds as modulators of AhR by means of in silico and in vitro methods. The general objective was concretized in specific aims distributed in the five Chapters of this Thesis as follow: Chapter 1. Review the literature on AhR mediated effects and the existing theoretical and experimental methods employed to study the structural and functional aspects of the receptor. Chapter 2. Develop and experimentally validate QSAR models to predict the AhR agonist activity of chemical compounds. Chapter 3. Analyze the dual effects of a set of benzothiazoles as AhR modulators and antimicrobial agents. Chapter 4. Evaluate a novel set of triarylmethane compounds as AhR ligands. Chapter 5. Study the AhR antagonism discovered in potentially harmful substances using computational methods.The aryl hydrocarbon receptor (AhR) is a cytoplasmatic sensor of diverse endogenous and exogenous substances. In a toxicological context, the former known as “dioxin receptor” has been investigated as a xenobiotic chemoreceptor and due to its roles in mediating carcinogenesis, endocrine disruption, among other immunological, hepatic, cardiovascular, and dermal toxicity mechanisms. The deep physiological implications of AhR in cellular proliferation, adhesion, division, differentiation, as well as in the reproductive, immunological and cardiovascular homeostasis have opened a new field of research in order to harness AhR’s pharmacological potential. Hence, AhR has become a therapeutic target of inflammatory, infectious, malignant, and immunological conditions. Toxicological and pharmacological fields could benefit from discovering novel AhR modulators to elucidate further on the chemical-biological implications of this crucial transcription factor. In this Thesis, the following objective was proposed in order to contribute to such understanding. General Objective: Evaluate diverse chemical compounds as modulators of AhR by means of in silico and in vitro methods. The general objective was concretized in specific aims distributed in the five Chapters of this Thesis as follow: Chapter 1. Review the literature on AhR mediated effects and the existing theoretical and experimental methods employed to study the structural and functional aspects of the receptor. Chapter 2. Develop and experimentally validate QSAR models to predict the AhR agonist activity of chemical compounds. Chapter 3. Analyze the dual effects of a set of benzothiazoles as AhR modulators and antimicrobial agents. Chapter 4. Evaluate a novel set of triarylmethane compounds as AhR ligands. Chapter 5. Study the AhR antagonism discovered in potentially harmful substances using computational methods

Computational Design, Synthesis, Characterization and Pharmacological Evaluation of Some Piperidine Derivatives

The Aurora kinase family is a collection of highly related serine/threonine kinases that functions as a key regulator of mitosis. In mammalian cells, Aurora has evolved into three related kinases known as Aurora-A, Aurora-B, and Aurora-C. These kinases are over expressed in a number of human cancers, and transfection studies have established Aurora-A as a bone fide oncogene. Because Aurora over expression is associated with malignancy, these kinases have been targeted for cancer therapy. So in the present study, it was decided to design some inhibitory lead compounds of Aurora kinase A as using computational tools like Catalyst (pharmacophore modeling) and GLIDE(structure based drug design/Docking). Then the designed compounds were synthesized and screened for anticancer studies. Eighty two Aurora A kinase inhibitors from Medicinal Chemistry Journals were selected for modeling studies based on chemical and biological diversity. The selected molecules were then divided into 21 training set molecules and 61 test set molecules. Using the training set molecules pharmacophore models (hypothesis) were generated in Hypogen (Catalyst). The most active molecule in the training set fits very well with the top scoring pharmacophore hypothesis. The best hypothesis consists of one hydrogen bond acceptor, one hydrophobic aliphatic and two ring aromatics. The best hypothesis Hypo-1 is characterized by the highest cost difference (58 bits), lowest RMS deviation (1.30) with a correlation of 0.94. The best pharmacophore hypothesis was used to screen the 61 Aurora A Kinase inhibitors in the Aurora kinase inhibitor data base. The model developed was shown to be a good model with 0.65 as Goodness of Hit score (GH) and a enrichment factor of 1.154. GLIDE was the docking program used for the structure based drug design. The 82 Aurora A inhibitors used for the pharmacophore studies were considered for docking study to develop comparative model. Out of the 82 inhibitors 21 were used in the training set. Crystal structure of Aurora A (PDB code: Imq4) was employed for the docking studies Structure based docking studies were carried out using Glide on Aurora A kinase inhibitors to the 3D structure of Aurora A kinase and generated 50 best docking poses. The best poses were selected based on the scoring functions and poses orientation with the active site amino acids. To get a better VS model, a MLR analysis was carried out using pharmacophore model and the docking scoring function. A combination of Pharmacophore model, GLIDE SP dock score gave a good model. The VS model obtained was further used to search the virtual library consisting of 10,000 structurally diversified molecules generated using fragment and knowledge based design, which yielded 300 molecules as potent Hits. The hits obtained were used for PASS prediction studies. A consensus was obtained between the docking scores and the PASS prediction values and finally 40 Hits were selected for synthesis and screening. The docking scores >-7 and prediction values above 0.6 were taken into consideration. PASS predicted some of the molecules to be active against colorectal cancer (1A-29A) and some other molecules to be inhibitors of phosphatase enzyme (P1-P11). A Drug likeness screening was carried out for all the 40 Hits including Lipinski rule of five and Toxicity assessment. All most all the compounds exhibited 2 violations of the Lipinski rule which was found to be normal with anticancer drugs. It is proven that kinase inhibitors in general have high molecular weight and LogP values. So these violations were accepted in the present study. In toxicity assessment all the compounds showed Green colour code except nine molecules having nitro and dimethyl amino substitution exhibiting mutagenicity and Tumorigenicity. But still these molecules were also included in the in vitro screening because so many active scaffolds have these substructures and their activities were also predicted to be good. These hits can be further refined to reduce the unwanted reactions by including detoxifying sub structures. The designed molecules have piperidine-4-ones scaffold attached with 2-Aminopyrimidine (1A-29A) and 2-Pyrazoline (P1-P11) substructures. The Schiff bases and Mannich bases of piperidine-4-ones were synthesized according to the synthetic scheme and characterized by IR,1H NMR,13 C NMR, COSY NMR and Mass spectroscopy. The compounds were subjected to in vitro anticancer studies in colorectal cell lines (1A-29A).The compounds 21A and 25A show the least IC50 values 0.01 and 0.01 respectively. The compound 21A maintains the same level of activity through out the working range (0.01-100 μM). There is no concentration related gradation in the activity profile. In the case of compound 25A, the peak activity is noted in the minimum concentration of 0.01 μM itself. Even though there is a decrease in the activity with increase in the concentration, the activity profile remained well above the required level. The other compounds showing significant activity are 3A, 10A, 13A and 26A. The compounds 6A, 9A and 16A show less activity with IC50 values in the range of 10 μM. All the other compounds showed moderate activity with IC50 values in the range of 1-5 μM. The compounds P1-P11 were subjected to phosphatase inhibition activity. The compounds P1-P11 were used in three concentration levels (50/125/250mcg). The absorbances of these solutions were measured at 620nm after carrying out the assay with Folin’s reagent. The concentrations of the phenol formed in these solutions were obtained from the standard graph of phenol. From the analysis of the data it can be seen that all the compounds P1-P11 are possessing phosphatase inhibition activity. In addition to that they also show a gradation in their dose response. All the compounds show less inhibition at 50 mcg, moderate inhibition at 125 mcg and a fairly good inhibition at 250 mcg. The compounds P1-P11 have 2-pyrazoline moiety as a sub structure in common. The compounds P1-P5 at 50 mcg concentration level shows very less inhibition. A concentration dependent increase in inhibitory activity was observed with P1, P2, P3, P4, and P5. That is better inhibitory activity was observed with higher concentrations (250 mcg). The compounds P6-P11 showed better inhibition in all the three concentration levels. In all these compounds the C3 of piperidin-4-one ring contain isopropyl group which may contribute to the increase in inhibitory activity. Of all compounds, the compound P3 at the 250mcg concentration level shows best activity. Thus the compounds P3, P7 and P11 can be further developed to get effective phosphatase inhibitors. By arriving at the leads 21A and 25A for anticancer activity, the aim of the work to develop leads for anticancer activity was fulfilled. These analogues can be novel templates for lead optimization purpose in cancer chemotherapy. To conclude the anticancer leads obtained in this study can be refined further to get a potent anticancer molecules. Drugs targeting multiple kinases have proven to be effective against treatment of various diseases. The activities of serine/threonine protein phosphatases needs further study, but it is clear that these enzymes are potential targets for novel therapeutics with applications in many diseases, including cancer, inflammatory diseases and neuro degeneration. Computational techniques have provided starting points for designing multiple inhibitors against individual targets using crystal structural information of kinases and pharmacophore of kinase inhibitors. So these techniques can be explored further to design new drug candidates for various diseases

In Silico Identification of a β2-Adrenoceptor Allosteric Site That Selectively Augments Canonical β2AR-Gs Signaling and Function

Activation of β2-adrenoceptors (β2ARs) causes airway smooth muscle (ASM) relaxation and bronchodilation, and β2AR agonists (β-agonists) are front-line treatments for asthma and other obstructive lung diseases. However, the therapeutic efficacy of β-agonists is limited by agonist-induced β2AR desensitization and noncanonical β2AR signaling involving β-arrestin that is shown to promote asthma pathophysiology. Accordingly, we undertook the identification of an allosteric site on β2AR that could modulate the activity of β-agonists to overcome these limitations. We employed the site identification by ligand competitive saturation (SILCS) computational method to comprehensively map the entire 3D structure of in silico-generated β2AR intermediate conformations and identified a putative allosteric binding site. Subsequent database screening using SILCS identified drug-like molecules with the potential to bind to the site. Experimental assays in HEK293 cells (expressing recombinant wild-type human β2AR) and human ASM cells (expressing endogenous β2AR) identified positive and negative allosteric modulators (PAMs and NAMs) of β2AR as assessed by regulation of β-agonist-stimulation of cyclic AMP generation. PAMs/NAMs had no effect on β-agonist-induced recruitment of β-arrestin to β2AR- or β-agonist-induced loss of cell surface expression in HEK293 cells expressing β2AR. Mutagenesis analysis of β2AR confirmed the SILCS identified site based on mutants of amino acids R131, Y219, and F282. Finally, functional studies revealed augmentation of β-agonist-induced relaxation of contracted human ASM cells and bronchodilation of contracted airways. These findings identify a allosteric binding site on the β2AR, whose activation selectively augments β-agonist-induced Gs signaling, and increases relaxation of ASM cells, the principal therapeutic effect of β-agonists

NMR and Computational Characterization of Protein Structure and Ligand Binding

Nuclear magnetic resonance (NMR) techniques combined with computational methods such as docking and cheminformatics were used to characterize protein structure and ligand binding. The thioredoxin system of Mycobacterium tuberculosis consists of a thioredoxin reductase and at least three thioredoxins. This system is responsible for maintaining the cellular protein thiol redox state in normal state. This maintenance is important as the bacterium is engulfed by the human macrophage. Here it is bombarded by reactive oxygen and nitrogen species in an attempt to disrupt normal cellular function in part by perturbing the protein thiols. To this end, the solution structures of the three thioredoxins, A, B, and C, in the oxidized state were solved by NMR. Additionally, the reduced form of thioredoxin C was solved as well. Docking and NMR chemical shit pertubation experiments show promise for the inhibition of the thioredoxin C-thioredoxin reductase catalytic turnover. Automated docking is the process of computationally predicting how tightly a ligand binds to a protein and the correct orientation. The docking of an in-house collection of 10,590 chemicals into a protein called dual specificity phosphatase 5 identified potential ligands. These compounds were characterized as inhibitors in a phosphatase assay and as ligands in NMR chemical shift perturbation experiments. Based on a promising lead compound, additional chemicals were identified using cheminformatics and subjected to the same experimental verification

Recent Trends in Pharmaceutical Analytical Chemistry

This book covers the most recent research trends and applications of Pharmaceutical Analytical Chemistry. The included topics range from the adulteration of dietary supplements, to the determination of drugs in biological samples with the aim to investigate their pharmacokinetic properties

Exploring Molecular Diversity: There is Plenty of Room at Markush's

L'estratègia de les etapes inicials del descobriment de fàrmacs està normalment basada en un procés anomenat hit-to-lead que implica un extens estudi entorn de la síntesi de derivats d'una molècula original que prèviament hagi mostrat certa activitat biològica davant d'una diana concreta. Per tant, aquest procés comporta la síntesi de molts anàlegs que descriurien una subquimioteca, que generalment evidencia que aquests estudis estan molt focalitzats al voltant de l'espai químic del compost original. Així i tot, quan aquesta molècula és finalment patentada, es descriu un espai químic molt més vast per mitjà d'estructures Markush donant per suposat que alguns dels seus derivats puguin presentar també activitat biològica. Tot i això, la presència d'aquestes estructures no implica la síntesi comprovada de tota la biblioteca molecular sinó només una petita mostra de la mateixa. La nostra hipòtesi és que hi ha una gran part de l’espai químic d’aquestes biblioteques que està sense explorar i pot amagar possibles candidats que poden fins i tot superar l’activitat del hit original. A través d'aquest projecte, es proposa una alternativa que sosté que una selecció racional de poques molècules – basat en l'agrupament segons semblança molecular – pot representar de manera més significativa l'espai químic establert, oferint la possibilitat d'explorar regions desconegudes que podrien amagar més potencial biològic. Després de revisar els darrers fàrmacs aprovats per la FDA en el període del 2008 al 2020 i la base de dades de molècules bioactives de ChEMBL, s'ha dut a terme una exploració de l'ampli espai químic resultant de molècules petites amb propietats similars a les dels medicaments per definir nous espais accessibles que podrien ocultar activitat. Els resultats obtinguts de set casos d'estudis reals han demostrat que tant la selecció racional com l’aleatòria representen més significativament les biblioteques combinatòries declarades a les patents, que les molècules descrites fins ara. S'han realitzat dos estudis pràctics que implementen aquesta metodologia suggerida per descriure millor l'espai químic del fàrmac antipalúdic Tafenoquina i del Dacomitinib, un inhibidor de tirosina cinases de segona generació per al tractament del càncer de pulmó de cèl·lules no petites. L’exploració de l’espai químic d’aquestes dues famílies ha portat a la síntesi racional de set anàlegs antipalúdics i vuit inhibidors de cinases que han mostrat interessants activitats inhibidores. Aquests resultats demostren que l'aplicació de la quimioinformàtica per a la selecció de biblioteques pot millorar la capacitat d'inspeccionar millor els conjunts de dades químiques per identificar nous compostos precandidats i representar grans biblioteques per a posteriors campanyes de reposicionament.La estrategia de las etapas iniciales del descubrimiento de fármacos está normalmente basada en un proceso denominado hit-to-lead que implica un extenso estudio entorno a la síntesis de derivados de una molécula original que previamente haya expresado cierta actividad biológica frente a una diana concreta. Por ende, este proceso conlleva la síntesis de muchos análogos que describirían una sublibrería química, la cual generalmente evidencia que estos estudios están muy focalizados alrededor del espacio químico del compuesto original. Aún y así, cuando esta molécula es finalmente patentada, se describe un espacio químico mucho más vasto por medio de estructuras Markush teorizando que algunos de sus derivados puedan presentar también actividad biológica. Sin embargo, la presencia de estas estructuras no implica la síntesis comprobada de toda la biblioteca molecular sino solo una pequeña muestra de la misma. Nuestra hipótesis es que hay una gran parte del espacio químico de estas bibliotecas que está sin explorar y puede ocultar posibles candidatos que pueden hasta superar la actividad del hit original. A través de este proyecto, se propone una alternativa que sostiene que una selección racional de pocas moléculas – fundada en el agrupamiento según su similitud química – puede representar de manera más significativa el espacio químico establecido, ofreciendo la posibilidad de explorar regiones desconocidas que podrían ocultar más potencial biológico. Después de revisar los últimos fármacos aprobados por la FDA en el período de 2008 a 2020 y la base de datos de moléculas bioactivas de ChEMBL, se ha llevado a cabo una exploración del amplio espacio químico resultante de moléculas pequeñas con propiedades similares a las de los medicamentos para definir nuevos espacios accesible que podrían ocultar actividad. Los resultados obtenidos de siete casos de estudios reales han demostrado que tanto la selección racional como la aleatoria representan más significativamente las bibliotecas combinatorias declaradas en las patentes que las moléculas descritas hasta la fecha. Se han desarrollado dos estudios prácticos que implementan esta metodología sugerida para describir mejor el espacio químico del fármaco antipalúdico Tafenoquina y Dacomitinib, un inhibidor de la tirosina quinasa de segunda generación para el tratamiento del cáncer de pulmón de células no pequeñas. La exploración del espacio químico de estas dos familias ha llevado a la síntesis racional de siete análogos antipalúdicos y ocho inhibidores de quinasas que han mostrado interesantes actividades inhibidoras. Estos resultados demuestran que la aplicación de la quimioinformática para la selección de bibliotecas puede mejorar la capacidad de inspeccionar mejor los conjuntos de datos químicos para identificar nuevos potenciales hits y representar grandes bibliotecas para fines de reposicionamiento.The early Drug Discovery strategy is commonly based on a hit-to-lead process which involves large research on the synthesis of derivatives of an original molecule that had previously shown biological activity against a specific biological target. Therefore, this process implies the synthesis of many analogs leading to the description of a chemical sub-library which generally leads to a highly focused study on the chemical space nearby the hit compound. However, when this drug is finally patented, a wider chemical space derived from a Markush structure is described, theorizing that some analogs within may present biological activity. Nevertheless, this claim involving the Markush structure does not imply the proven synthesis of all the chemical library but just a small population of it. We hypothesize that there is a great part of the chemical space of these libraries that is unexplored and can hide potential lead candidates which may even surpass the activity of the original hit. Through this project, an alternative is proposed claiming that a rational selection of a short sample of small molecules – founded on similarity-based clustering – can represent more significatively the stated chemical space offering the possibility to explore the unknown space that could hide more potential biological activity. After a review on the latest approved drugs by the FDA in the period from 2008 to 2020 and the ChEMBL database of bioactive molecules, an exploration of the resulting wide chemical space of small molecules with drug-like properties has been assessed in order to define accessible spots that might hide biological activity. The obtained results from seven real cases of study have proven that random and rationally selected molecules represent more significantly the combinatorial libraries stated in the patents rather than the reported molecules until date. Furthermore, two practical studies implementing our suggested methodology have been developed to better describe the chemical space of the antimalarial drug Tafenoquine and Dacomitinib, a second-generation tyrosine kinase inhibitor for non-small-cell lung cancer treatment. The assessment driven by a better chemical space exploration of these two families have led to the rational synthesis of seven antimalarial analogs and eight kinase inhibitors which have shown interesting inhibitory activities. Our results evince that the application of cheminformatics for library selection may improve the ability to better inspect chemical datasets in order to identify new potential hits and represent large libraries for further reprofiling purposes

Graph-Based Approaches to Protein StructureComparison - From Local to Global Similarity

The comparative analysis of protein structure data is a central aspect of structural bioinformatics. Drawing upon structural information allows the inference of function for unknown proteins even in cases where no apparent homology can be found on the sequence level. Regarding the function of an enzyme, the overall fold topology might less important than the specific structural conformation of the catalytic site or the surface region of a protein, where the interaction with other molecules, such as binding partners, substrates and ligands occurs. Thus, a comparison of these regions is especially interesting for functional inference, since structural constraints imposed by the demands of the catalyzed biochemical function make them more likely to exhibit structural similarity. Moreover, the comparative analysis of protein binding sites is of special interest in pharmaceutical chemistry, in order to predict cross-reactivities and gain a deeper understanding of the catalysis mechanism. From an algorithmic point of view, the comparison of structured data, or, more generally, complex objects, can be attempted based on different methodological principles. Global methods aim at comparing structures as a whole, while local methods transfer the problem to multiple comparisons of local substructures. In the context of protein structure analysis, it is not a priori clear, which strategy is more suitable. In this thesis, several conceptually different algorithmic approaches have been developed, based on local, global and semi-global strategies, for the task of comparing protein structure data, more specifically protein binding pockets. The use of graphs for the modeling of protein structure data has a long standing tradition in structural bioinformatics. Recently, graphs have been used to model the geometric constraints of protein binding sites. The algorithms developed in this thesis are based on this modeling concept, hence, from a computer scientist's point of view, they can also be regarded as global, local and semi-global approaches to graph comparison. The developed algorithms were mainly designed on the premise to allow for a more approximate comparison of protein binding sites, in order to account for the molecular flexibility of the protein structures. A main motivation was to allow for the detection of more remote similarities, which are not apparent by using more rigid methods. Subsequently, the developed approaches were applied to different problems typically encountered in the field of structural bioinformatics in order to assess and compare their performance and suitability for different problems. Each of the approaches developed during this work was capable of improving upon the performance of existing methods in the field. Another major aspect in the experiments was the question, which methodological concept, local, global or a combination of both, offers the most benefits for the specific task of protein binding site comparison, a question that is addressed throughout this thesis

Development and optimisation of computational tools for drug discovery

The aim of my PhD project was the development, optimisation, and implementation of new in silico virtual screening protocols. Specifically, this thesis manuscript is divided into three main parts, presenting some of the papers published during my doctoral work. The first one, here named CHEMOMETRIC PROTOCOLS IN DRUG DISCOVERY, is about the optimisation and application of an in house developed chemometric protocol. This part has been entirely developed at the University of Palermo - STEBICEF Department - under the guide of my supervisors. During the development of this part I have personally worked on the tuning and optimisation of the algorithm and on the docking campaigns to obtain molecule conformaitons. The second part, THE APPLICATION OF MOLECULAR DYNAMICS TO VIRTUAL SCREENING, presents a new approach to virtual screening, in particular the attention is focused on different approaches to the application of protein flexibility and dynamics to virtual screening. This part, has been carried out in cooperation with the University of Vienna - Department of Pharmaceutical Chemistry. For these works I have worked in the development of the general workflow, to a lesser extent to the programming (coding) part of the applications used and I mainly focused on the realisation of the screening campaigns and results interpretation. The third and last part, COMPUTATIONAL CHEMISTRY IN POLY-PHARMACOLOGY AND DRUG REPURPOSING, concerns the study of the in silico methods applied to two main topics of the drug discovery process, such as the drug repurposing and the polypharmacology. In this part I will briefly describe what published in two reviews dealing to the above mentioned topics. In conclusion during this doctoral project, I have demonstrated how the use of in silico tools can be useful in the drug discovery process. The Chemometric protocols developed and optimised represent in fact a helpful strategy to use for target fishing. Whereas, the application of molecular dynamics to virtual screening, especially for pharmacophore modelling, is a new way to deepen crucial features to be adopted in the search of new putative active compounds.The aim of my PhD project was the development, optimisation, and implementation of new in silico virtual screening protocols. Specifically, this thesis manuscript is divided into three main parts, presenting some of the papers published during my doctoral work. The first one, here named CHEMOMETRIC PROTOCOLS IN DRUG DISCOVERY, is about the optimisation and application of an in house developed chemometric protocol. This part has been entirely developed at the University of Palermo - STEBICEF Department - under the guide of my supervisors. During the development of this part I have personally worked on the tuning and optimisation of the algorithm and on the docking campaigns to obtain molecule conformaitons. The second part, THE APPLICATION OF MOLECULAR DYNAMICS TO VIRTUAL SCREENING, presents a new approach to virtual screening, in particular the attention is focused on different approaches to the application of protein flexibility and dynamics to virtual screening. This part, has been carried out in cooperation with the University of Vienna - Department of Pharmaceutical Chemistry. For these works I have worked in the development of the general workflow, to a lesser extent to the programming (coding) part of the applications used and I mainly focused on the realisation of the screening campaigns and results interpretation. The third and last part, COMPUTATIONAL CHEMISTRY IN POLY-PHARMACOLOGY AND DRUG REPURPOSING, concerns the study of the in silico methods applied to two main topics of the drug discovery process, such as the drug repurposing and the polypharmacology. In this part I will briefly describe what published in two reviews dealing to the above mentioned topics. In conclusion during this doctoral project, I have demonstrated how the use of in silico tools can be useful in the drug discovery process. The Chemometric protocols developed and optimised represent in fact a helpful strategy to use for target fishing. Whereas, the application of molecular dynamics to virtual screening, especially for pharmacophore modelling, is a new way to deepen crucial features to be adopted in the search of new putative active compounds