Solvents to Fragments to Drugs: MD Applications in Drug Design

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates

Solvents to fragments to drugs: MD applications in drug design

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.Fil: Defelipe, Lucas Alfredo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Arcon, Juan Pablo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Modenutti, Carlos Pablo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Marti, Marcelo Adrian. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Turjanski, Adrian. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Barril, Xavier. Institucio Catalana de Recerca I Estudis Avancats

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening

Virtual screening of large compound databases, looking for potential ligands of a target protein, is a major tool in computer-aided drug discovery. Throughout the years, different techniques such as similarity searching, pharmacophore matching, or molecular docking have been applied with the aim of finding hit compounds showing appreciable affinity. Molecular dynamics simulations in mixed solvents have been shown to identify hot spots relevant for protein-drug interaction, and implementations based on this knowledge were developed to improve pharmacophore matching of small molecules, binding free-energy estimations, and docking performance in terms of pose prediction. Here, we proved in a retrospective manner that cosolvent-derived pharmacophores from molecular dynamics (solvent sites) improve the performance of docking-based virtual screening campaigns. We applied a biased docking scheme based on solvent sites to nine relevant target proteins that have a set of known ligands or actives and compounds that are, presumably, nonbinders (decoys). Our results show improvement in virtual screening performance compared to traditional docking programs both at a global level, with up to 35% increase in areas under the receiver operating characteristic curve, and in early stages, with up to a 7-fold increase in enrichment factors at 1%. However, the improvement in pose prediction of actives was less profound. The presented application makes use of the AutoDock Bias method and is the only cosolvent-derived pharmacophore technique that employs its knowledge both in the ligand conformational search algorithm and the final affinity scoring for virtual screening purposes.Fil: Arcon, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Defelipe, Lucas Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Lopez, Elias Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Burastero, Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Modenutti, Carlos Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Barril, Xavier. Universidad de Barcelona; EspañaFil: Marti, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Turjanski, Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentin

Molecular dynamics and virtual screening approaches in drug discovery

Computer-aided drug discovery (CADD) methods are now routinely used in the preclinical phase of drug development. Powerful high-performance computing facilities and the extremely fast CADD methods constantly scale up the coverage of drug-like chemical space achievable in rational drug development. In this thesis, CADD approaches were applied to address several early-phase drug discovery problems. Namely, small molecule binding site detection on a novel target protein, virtual screening (VS) of molecular databases, and characterization of small molecule interactions with metabolic enzymes were studied. Various CADD methods, including molecular dynamics (MD) simulations in mixed solvents, molecular docking, and binding free energy calculations, were employed. Co-solvent MD simulations detected biologically relevant binding sites and provided guidance for screening potential protein-protein interaction inhibitors for a crucial protein of the SARS-CoV-2. VS with fragment- and negative image-based (F-NIB) models identified three active and structurally novel inhibitors of the putative drug target phosphodiesterase 10A. MD simulations and docking provided detailed insights on the effects of active site structural flexibility and variation on the binding and resultant metabolism of small molecules with the cytochrome P450 enzymes. The results presented in this thesis contribute to the increasing evidence that supports employment and further development of CADD approaches in drug discovery. Ultimately, rational drug development coupled with CADD may enable higher quality drug candidates to the human studies in the future, reducing the risk of financially and temporally costly clinical failure. KEYWORDS: Structure-based drug development, Computer-aided drug discovery (CADD), Molecular dynamics (MD) simulation, Virtual screening (VS), Fragmentand negative image-based (F-NIB) model, Structure-activity relationship (QSAR), Cytochrome P450 ligand binding predictionMolekyylidynamiikka- ja virtuaaliseulontamenetelmät lääkeaine-etsinnässä Tietokoneavusteista lääkeaine-etsintää käytetään nykyisin yleisesti prekliinisessä lääketutkimuksessa. Suurteholaskenta ja äärimmäisen nopeat tietokoneavusteiset lääkeaine-etsintämenetelmät mahdollistavat jatkuvasti kattavamman lääkkeenkaltaisten molekyylien kemiallisen avaruuden seulonnan. Tässä väitöskirjassa tietokonepohjaisia menetelmiä hyödynnettiin lääketutkimuksen prekliiniseen vaiheeseen liittyvissä tyypillisissä tutkimusongelmissa. Työhön kuului pienmolekyylien sitoutumisalueiden tunnistus uuden kohdeproteiinin rakenteesta, molekyylitietokantojen virtuaaliseulonta sekä pienmolekyylien ja metabolian entsyymien välisten vuorovaikutusten tietokonemallinnus. Työssä käytettiin useita tietokoneavusteisen lääkeaine-etsinnän menetelmiä, sisältäen molekyylidynamiikkasimulaatiot (MD-simulaatiot) vaihtuvissa liuottimissa, molekulaarisen telakoinnin ja sitoutumisenergian laskennan. Orgaanisen liuottimen ja veden sekoituksessa tehdyt MD-simulaatiot tunnistivat biologisesti merkittäviä sitoutumisalueita SARS-CoV-2:n tärkeästä proteiinista ja ohjasivat infektioon liittyvän proteiini-proteiinivuorovaikutuksen potentiaalisten estäjien etsintää. Virtuaaliseulonnalla tunnistettiin kolme rakenteellisesti uudenlaista tunnetun lääkekehityskohteen, fosfodiesteraasi 10A:n, estäjää hyödyntäen fragmentti- ja negatiivikuvamalleja. MD-simulaatiot ja telakointi tuottivat yksityiskohtaista tietoa sytokromi P450 entsyymien aktiivisen kohdan rakenteen jouston ja muutosten vaikutuksesta pienmolekyylien sitoutumiseen ja metaboliaan. Tämän väitöskirjan tulokset tukevat kasvavaa todistusaineistoa tietokoneavusteisen lääkeaine-etsinnän käytön ja kehityksen hyödyllisyydestä prekliinisessä lääketutkimuksessa. Tietokoneavusteinen lääkeaine-etsintä voi lopulta mahdollistaa korkeampilaatuisten lääkekandidaattien päätymisen ihmiskokeisiin, pienentäen taloudellisesti ja ajallisesti kalliin kliinisen tutkimuksen epäonnistumisen riskiä. AVAINSANAT: Rakennepohjainen lääkeainekehitys, Tietokoneavusteinen lääkeaine-etsintä, Molekyylidynamiikkasimulaatio (MD-simulaatio), Virtuaaliseulonta, Fragmentti- ja negatiivikuvamalli, Rakenne-aktiivisuussuhde, Sytokromi P450 ligandien sitoutumisen ennustu

Technological developments in Virtual Screening for the discovery of small molecules with novel mechanisms of action

Programa de Doctorat en Recerca, Desenvolupament i Control de Medicaments[eng] Advances in structural and molecular biology have favoured the rational development of novel drugs thru structure-based drug design (SBDD). Particularly, computational tools have proven to be rapid and efficient tools for hit discovery and optimization. The main motivation of this thesis is to improve and develop new methods in the area of computer-based drug discovery in order to study challenging targets. Specifically, this thesis is focused on docking and Virtual Screening (VS) methodologies to be able to exploit non-standard sites, like protein-protein interfaces or allosteric sites, and discover bioactive molecules with novel mechanisms of action. First, I developed an automatic pipeline for binding mode prediction that applies knowledge- based restraints and validated the approach by participating in the CELPP Challenge, a blind pose prediction challenge. The aim of the first VS in this thesis is to find small molecules able to not only disrupt the RANK-RANKL interaction but also inhibit the constitutive activation of the receptor. With a combination of computational, biophysical, and cell-based assays we were able to identify the first small molecule binders for RANK that could be used as a treatment for Triple Negative Breast Cancer. When working with challenging targets, or with non-standard mechanisms of action, the relationship between binding and the biological response is unpredictable, because the biological response (if any) will depend on the biological function of the particular allosteric site, which is generally unknown. For this reason, we then tested the applicability of the combination of ultrahigh-throughput VS with low-throughput high content assay. This allowed us to characterize a novel allosteric pocket in PTEN and also describe the first allosteric modulators for this protein. Finally, as the accessible Chemical Space grows at a rapid pace, we developed an algorithm to efficiently explore ultra-large Chemical Collections using a Bottom-up approach. We prospectively validated the approach in BRD4 and identified novel BRD4 inhibitors with an affinity comparable to advanced drug candidates for this target.[spa] Els avenços en biologia estructural i molecular han afavorit el desenvolupament racional de nous fàrmacs a través del disseny de fàrmacs basat en l'estructura (SBDD). En particular, les eines computacionals han demostrat ser ràpides i eficients per al descobriment i l'optimització de fàrmacs. La principal motivació d'aquesta tesi és millorar i desenvolupar nous mètodes en l'àrea del descobriment de fàrmacs per ordinador per tal d'estudiar proteïnes complexes. Concretament, aquesta tesi se centra en les metodologies d'acoblament i de cribratge virtual (CV) per poder explotar llocs no estàndard, com interfícies proteïna-proteïna o llocs al·lostèrics, i descobrir molècules bioactives amb nous mecanismes d'acció. En primer lloc, vaig desenvolupar un protocol automàtic per a la predicció del mode d’unió aplicant restriccions basades en el coneixement i vaig validar l'enfocament participant en el repte CELPP, un repte de predicció del mode d’unió a cegues. L'objectiu del primer CV d'aquesta tesi és trobar petites molècules capaces no només d'interrompre la interacció RANK-RANKL sinó també d'inhibir l'activació constitutiva del receptor. Amb una combinació d'assajos computacionals, biofísics i basats en cèl·lules, vam poder identificar les primeres molècules petites per a RANK que es podrien utilitzar com a tractament per al càncer de mama triple negatiu. Quan es treballa amb proteïnes complexes, o amb mecanismes d'acció no estàndard, la relació entre la unió i la resposta biològica és impredictible, perquè la resposta biològica (si n'hi ha) dependrà de la funció biològica del lloc al·lostèric particular, que generalment és desconeguda. Per aquest motiu, després vam provar l'aplicabilitat de la combinació de CV d'alt rendiment amb assaig de contingut alt de baix rendiment. Això ens va permetre caracteritzar un nou lloc d’unió al·lostèric en PTEN i també descriure els primers moduladors al·lostèrics d'aquesta proteïna. Finalment, a mesura que l'espai químic accessible creix a un ritme ràpid, hem desenvolupat un algorisme per explorar de manera eficient col·leccions de productes químics molt grans mitjançant un enfocament de baix a dalt. Vam validar aquest enfocament amb BRD4 i vam identificar nous inhibidors de BRD4 amb una afinitat comparable als candidats a fàrmacs més avançats per a aquesta proteïna

Technological developments in Virtual Screening for the discovery of small molecules with novel mechanisms of action

[eng] Advances in structural and molecular biology have favoured the rational development of novel drugs thru structure-based drug design (SBDD). Particularly, computational tools have proven to be rapid and efficient tools for hit discovery and optimization. The main motivation of this thesis is to improve and develop new methods in the area of computer-based drug discovery in order to study challenging targets. Specifically, this thesis is focused on docking and Virtual Screening (VS) methodologies to be able to exploit non-standard sites, like protein-protein interfaces or allosteric sites, and discover bioactive molecules with novel mechanisms of action. First, I developed an automatic pipeline for binding mode prediction that applies knowledge- based restraints and validated the approach by participating in the CELPP Challenge, a blind pose prediction challenge. The aim of the first VS in this thesis is to find small molecules able to not only disrupt the RANK-RANKL interaction but also inhibit the constitutive activation of the receptor. With a combination of computational, biophysical, and cell-based assays we were able to identify the first small molecule binders for RANK that could be used as a treatment for Triple Negative Breast Cancer. When working with challenging targets, or with non-standard mechanisms of action, the relationship between binding and the biological response is unpredictable, because the biological response (if any) will depend on the biological function of the particular allosteric site, which is generally unknown. For this reason, we then tested the applicability of the combination of ultrahigh-throughput VS with low-throughput high content assay. This allowed us to characterize a novel allosteric pocket in PTEN and also describe the first allosteric modulators for this protein. Finally, as the accessible Chemical Space grows at a rapid pace, we developed an algorithm to efficiently explore ultra-large Chemical Collections using a Bottom-up approach. We prospectively validated the approach in BRD4 and identified novel BRD4 inhibitors with an affinity comparable to advanced drug candidates for this target.[cat] Els avenços en biologia estructural i molecular han afavorit el desenvolupament racional de nous fàrmacs a través del disseny de fàrmacs basat en l'estructura (SBDD). En particular, les eines computacionals han demostrat ser ràpides i eficients per al descobriment i l'optimització de fàrmacs. La principal motivació d'aquesta tesi és millorar i desenvolupar nous mètodes en l'àrea del descobriment de fàrmacs per ordinador per tal d'estudiar proteïnes complexes. Concretament, aquesta tesi se centra en les metodologies d'acoblament i de cribratge virtual (CV) per poder explotar llocs no estàndard, com interfícies proteïna-proteïna o llocs al·lostèrics, i descobrir molècules bioactives amb nous mecanismes d'acció. En primer lloc, vaig desenvolupar un protocol automàtic per a la predicció del mode d’unió aplicant restriccions basades en el coneixement i vaig validar l'enfocament participant en el repte CELPP, un repte de predicció del mode d’unió a cegues. L'objectiu del primer CV d'aquesta tesi és trobar petites molècules capaces no només d'interrompre la interacció RANK-RANKL sinó també d'inhibir l'activació constitutiva del receptor. Amb una combinació d'assajos computacionals, biofísics i basats en cèl·lules, vam poder identificar les primeres molècules petites per a RANK que es podrien utilitzar com a tractament per al càncer de mama triple negatiu. Quan es treballa amb proteïnes complexes, o amb mecanismes d'acció no estàndard, la relació entre la unió i la resposta biològica és impredictible, perquè la resposta biològica (si n'hi ha) dependrà de la funció biològica del lloc al·lostèric particular, que generalment és desconeguda. Per aquest motiu, després vam provar l'aplicabilitat de la combinació de CV d'alt rendiment amb assaig de contingut alt de baix rendiment. Això ens va permetre caracteritzar un nou lloc d’unió al·lostèric en PTEN i també descriure els primers moduladors al·lostèrics d'aquesta proteïna. Finalment, a mesura que l'espai químic accessible creix a un ritme ràpid, hem desenvolupat un algorisme per explorar de manera eficient col·leccions de productes químics molt grans mitjançant un enfocament de baix a dalt. Vam validar aquest enfocament amb BRD4 i vam identificar nous inhibidors de BRD4 amb una afinitat comparable als candidats a fàrmacs més avançats per a aquesta proteïna

Studying protein-ligand interactions using a Monte Carlo procedure

[eng] Biomolecular simulations have been widely used in the study of protein-ligand interactions; comprehending the mechanisms involved in the prediction of binding affinities would have a significant repercussion in the pharmaceutical industry. Notwithstanding the intrinsic difficulty of sampling the phase space, hardware and methodological developments make computer simulations a promising candidate in the resolution of biophysically relevant problems. In this context, the objective of the thesis is the development of a protocol that permits studying protein-ligand interactions, in view to be applied in drug discovery pipelines. The author contributed to the rewriting PELE, our Monte Carlo sampling procedure, using good practices of software development. These involved testing, improving the readability, modularity, encapsulation, maintenance and version control, just to name a few. Importantly, the recoding resulted in a competitive cutting-edge software that is able to integrate new algorithms and platforms, such as new force fields or a graphical user interface, while being reliable and efficient. The rest of the thesis is built upon this development. At this point, we established a protocol of unbiased all-atom simulations using PELE, often combined with Markov (state) Models (MSM) to characterize the energy landscape exploration. In the thesis, we have shown that PELE is a suitable tool to map complex mechanisms in an accurate and efficient manner. For example, we successfully conducted studies of ligand migration in prolyl oligopeptidases and nuclear hormone receptors (NHRs). Using PELE, we could map the ligand migration and binding pathway in such complex systems in less than 48 hours. On the other hand, with this technique we often run batches of 100s of simulations to reduce the wall-clock time. MSM is a useful technique to join these independent simulations in a unique statistical model, as individual trajectories only need to characterize the energy landscape locally, and the global characterization can be extracted from the model. We successfully applied the combination of these two methodologies to quantify binding mechanisms and estimate the binding free energy in systems involving NHRs and tyorsinases. However, this technique represents a significant computational effort. To reduce the computational load, we developed a new methodology to overcome the sampling limitations caused by the ruggedness of the energy landscape. In particular, we used a procedure of iterative simulations with adaptive spawning points based on reinforcement learning ideas. This permits sampling binding mechanisms at a fraction of the cost, and represents a speedup of an order of magnitude in complex systems. Importantly, we show in a proof-of-concept that it can be used to estimate absolute binding free energies. Overall, we hope that the methodologies presented herein help streamline the drug design process.[spa] Las simulaciones biomoleculares se han usado ampliamente en el estudio de interacciones proteína-ligando. Comprender los mecanismos involucrados en la predicción de afinidades de unión tiene una gran repercusión en la industria farmacéutica. A pesar de las dificultades intrínsecas en el muestreo del espacio de fases, mejoras de hardware y metodológicas hacen de las simulaciones por ordenador un candidato prometedor en la resolución de problemas biofísicos con alta relevancia. En este contexto, el objetivo de la tesis es el desarrollo de un protocolo que introduce un estudio más eficiente de las interacciones proteína-ligando, con vistas a diseminar PELE, un procedimiento de muestreo de Monte Carlo, en el diseño de fármacos. Nuestro principal foco ha sido sobrepasar las limitaciones de muestreo causadas por la rugosidad del paisaje de energías, aplicando nuestro protocolo para hacer analsis detallados a nivel atomístico en receptores nucleares de hormonas, receptores acoplados a proteínas G, tirosinasas y prolil oligopeptidasas, en colaboración con una compañía farmacéutica y de varios laboratorios experimentales. Con todo ello, esperamos que las metodologías presentadas en esta tesis ayuden a mejorar el diseño de fármacos