The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe

Identification of novel 2-benzoxazolinone derivatives with specific inhibitory activity against the HIV-1 nucleocapsid protein

In this report, we present a new benzoxazole derivative endowed with inhibitory activity against the HIV-1 nucleocapsid protein (NC). NC is a 55-residue basic protein with nucleic acid chaperone properties, which has emerged as a novel and potential pharmacological target against HIV-1. In the pursuit of novel NC-inhibitor chemotypes, we performed virtual screening and in vitro biological evaluation of a large library of chemical entities. We found that compounds sharing a benzoxazolinone moiety displayed putative inhibitory properties, which we further investigated by considering a series of chemical analogues. This approach provided valuable information on the structure-activity relationships of these compounds and, in the process, demonstrated that their anti-NC activity could be finely tuned by the addition of specific substituents to the initial benzoxazolinone scaffold. This study represents the starting point for the possible development of a new class of antiretroviral agents targeting the HIV-1 NC protein

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

Molecular docking: Shifting paradigms in drug discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence

Recent advances in in silico target fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies

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

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued e orts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature

Designing algorithms to aid discovery by chemical robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery