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Structure-based virtual screening approaches have the ability to dramatically reduce the time and costs associated to the discovery of new drug candidates. Studies have shown that the true hit rate of virtual screenings improves with the scale of the screened ligand libraries. Therefore, we have recently developed an open source drug discovery platform (VirtualFlow), which is able to routinely carry out ultra-large virtual screenings. One of the primary challenges of molecular docking is the circumstance when the protein is highly dynamic or when the structure of the protein cannot be captured by a static pose. To accommodate protein dynamics, we report the extension of VirtualFlow to allow the docking of ligands using a grey wolf optimization algorithm using the docking program GWOVina, which substantially improves the quality and efficiency of flexible receptor docking compared to AutoDock Vina. We demonstrate the linear scaling behavior of VirtualFlow utilizing GWOVina up to 128 000 CPUs. The newly supported docking method will be valuable for drug discovery projects in which protein dynamics and flexibility play a significant role.

Computational science has the potential to solve most of the problems which pharmaceutical research is facing these days. In this field the most pivotal property is arguably the free energy of binding. Yet methods to predict this quantity with sufficient accuracy, reliability and efficiency remain elusive, and are thus not yet able to replace experimental determinations, which remains one of the unattained holy grails of computer-aided drug design (CADD). The situation is similar for methods which are used to identify promising new drug candidates with high binding affinity, which resembles a closely related endeavor in this field. In this thesis the development of a new free energy method (QSTAR) was in the focus. It is able to explicitly take into account the quantum nature of atomic nuclei which so far was not done in binding free energy simulations of biomolecular systems. However, it can be expected to play a substantial role in such systems in particular due to the abundance of hydrogen atoms which posses one of the strongest nuclear delocalizations of all atoms. To take these nuclear quantum effects into account Feynman’s path integral formulation is used and combined in a synergistic way with a novel alchemical transformation scheme. QSTAR makes also available the first readily available single topology approach for electronic structure methods (ESMs). Moreover, an extended alchemical scheme for relative binding free energies was developed to address van der Waals endpoint problems. QSTAR and the alchemical schemes were implemented in HyperQ, a new free energy simulation suite which is highly automated and scalable. Most ESMs methods become soon prohibitively expensive with the size of the system, a restriction which can be circumvented by quantum mechanics/molecular mechanics (QM/MM) methods. In order to be able to apply QSTAR together with ESMs on biomolecular systems an enhanced QM/MM scheme was developed. It is a method for diffusive systems based on restraining potentials, and allows to define QM regions of customizable shape while being computationally fast. It was implemented in a novel client for i-PI, and together with HyperQ allows to carry out free energy simulations of biomolecular systems with potentials of very high accuracy. One of the most promising ways to identify new hit compounds in CADD is provided by structure- based virtual screenings (SBVSs) which make use of free energy methods. In this thesis it is argued that the larger the scale of virtual screenings the higher their success. And a novel workflow system was developed called Virtual Flow, allowing to carry out SBVS-related tasks on computer clusters with virtually perfect scaling behavior and no practically relevant bounds regarding the number of nodes/CPUs. Two versions were implemented, VFLP and VFVS, dedicated to the preparation of large ligand databases and for carrying out the SBVS procedure itself. As a primary application of the new methods and software a dedicated drug design project was started involving three regions on the novel target EBP1, expected to be located on protein-protein interfaces which are extremely challenging to inhibit. Three multistage SBVSs were carried out each involving more than 100 million compounds. Subsequent experimental binding assays indicated a remarkably high true hit rate of above 30 %. Subsequent fluorescence microscopy of one selected compound exhibited favorable biological activities in cancer cells. Other applied projects included computational hit and lead discovery for several other types of anti- cancer drugs, anti-Herpes medications, as well as antibacterials., Die Simulationswissenschaft, auch wissenschaftliches Rechnen genannt, hat das Potential viele der aktuellen Probleme der pharmazeutischen Forschung zu lösen. Eine der zentralen physikalischen Größen in diesem Gebiet ist die Bindungsenergie zwischen Molekülen. Eines der Hauptziele in der computergestützter Wirkstoffentwicklung (CADD) ist es, diese Größe mit solcher Genauigkeit, Verlässlichkeit und Effizienz vorherzusagen, dass dassexperimentelle Bestimmungen nicht mehr notwendig sind. Jedoch sind derartige Methoden derzeit noch nicht verfügbar. Ein Schwerpunkt der vorliegenden Arbeit war die Entwicklung einer neuen Methode (QSTAR) zur Vorhersage von freien Energien. Diese Methode ist fähig die quantenmechanische Natur von Atomkernen explizit zu berücksichtigen, was in bisherigen Simulationen für freie Bindungsenergien in biomolekularen Simulationen vernachlässig wurde. Es kann angenommen werden, dass die quantenmechanische Delokalisation der Atomkerne in solchen Systemen eine erhebliche Rolle spielen kann, vor allem aufgrund der Wasserstoffatome, welche zu den Atomarten mit den stärksten Quantendelokalisationen gehören. Um diese nuklearen Quanteneffekte zu berücksichtigen, wurde der Feynman’sche Pfadintegral Formalismus verwendet und synergetisch mit einem neuen alchemischen Transformationsschema kombiniert. QSTAR stellt auch den ersten direkt anwendbaren Einfach- Topologieansatz für Elektronenstrukturmodelle (ESMs) zur Verfügung. Des Weiteren wurde ein erweitertes alchemisches Schema für relative freie Bindungsenergien entwickelt, um das van der Waals Endpunktproblem zu umgehen. QSTAR und die alchemischen Schemen wurden in HyperQ implementiert, einer neuen skalierbaren Software, welche in der Lage ist, Simulationen der freien Energie automatisch durchzuführen. Die meisten ESMs werden mit zunehmender Größe des Systems schnell prohibitiv teuer. Dies ist eine Einschränkung, welche mit QM/MM Methoden umgangen werden kann. Um QSTAR mit ESMs on auf biomolekulare Systeme anwenden zu können, wurde ein erweitertes QM/MM Schema entwickelt. Dieses Schema ist eine Methode für diffusive Systeme, welche auf Rückhaltepotentialen beruht. Diese erlaubt, QM Regionen von angepasster Form zu definieren, und ist rechnerisch sehr effizient. Die Methode wurde in einem neuen Klienten für i-PI implementiert, und sie ermöglicht es, Simulationen der freien Energie von biomolekularen Systemen mit einer sehr hohen Genauigkeit durchzuführen. Einer der vielversprechendsten Ansätze um neue Hit-Kandidaten im CADD zu identifizieren sind strukturbasierte virtuelle Screenings (SBVS) welche Methoden zur Schätzung von freien Bindungsenergien nutzen. In dieser Arbeit wird argumentiert, dass je umfangreicher das virtuelle Screening ist desto besser die Ergebnisse im Sinne der Bindungsaffinität sowie der Erfolgsrate werden. Ein neues Workflowsystem, Virtual Flow, wurde entwickelt, welches erlaubt Aufgaben im Zusammenhang von SBVSs mit fast perfektem Skalierungsverhalten ohne praktisch relevante Grenzen bezüglich der Anzahl der Knoten/CPUs auf Computerclustern durchzuführen. Zwei Versionen wurden bisher implementiert, VFLP und VFVS, welche spezialisiert sind auf die Aufbereitung von Moleküldatenbanken sowie die virtuelle Screeningprozedur selbst. Eine neues Wirkstoffentwicklungsprojekt wurde begonnen, um die neuen Methoden und Software unter realistischen Bedingungen zu testen und anzuwenden. Das Ziel dieses Projektes ist es, mögliche Inhibitoren für drei Regionen an der Oberfläche des Proteins EBP1 zu identifizieren, welche sich aller Erkenntnis nach auf Protein-Protein-Interfaces befinden und eine große Herausforderung darstellen. Drei zweistufige virtuelle Screenings wurden ausgeführt mit jeweils über 100 Millionen Molekülen. Nachfolgende Bindungsexperimente deuten auf eine relativ hohe Hitrate von über 30 % hin, und Fluoreszenzmikrosopie von mindestens einem Molekül weist auf gewünschte Effekte in Krebszellen hin. Weitere Projekte, in welchen die neuen Methoden und Software für die computergestützte Hit/Lead-Identifizierung angewendet wurden, beinhalteten die Zielmoleküle SHP2 (Krebs), eIF4E (Krebs), MED15 (Krebs), UL50/UL53 (Herpes), KEAP1 (Krebs), und die Peptidoglykane (Antibiotika).

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC 50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed in silico screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 in silico hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development., Competing Interests: Alexander Chuprina, Dmytro Radchenko,  and Iryna Iavniuk work for Enamine, Kyiv Ukraine. Igor Dziuba works for UkrOrgSyntez Ltd, Kyiv Ukraine. Olga Tarkhanova, Alla Plekhova, and Yurii Moroz work for Chemspace Kyiv, Ukraine. Enamine, UkrOrgSyntez, and Chemspace are companies that are involved in the synthesis and distribution of drug-like compounds. Yurii Moroz is a scientific advisor for Enamine.