Multi-and many-objective optimization: present and future in de novo drug design.

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.

Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants.

The COVID-19 caused by the SARS-CoV-2 virus was declared a pandemic disease in March 2020 by the World Health Organization (WHO). Structure-Based Drug Design strategies based on docking methodologies have been widely used for both new drug development and drug repurposing to find effective treatments against this disease. In this work, we present the developments implemented in the DockThor-VS web server to provide a virtual screening (VS) platform with curated structures of potential therapeutic targets from SARS-CoV-2 incorporating genetic information regarding relevant non-synonymous variations. The web server facilitates repurposing VS experiments providing curated libraries of currently available drugs on the market. At present, DockThor-VS provides ready-for-docking 3D structures for wild type and selected mutations for Nsp3 (papain-like, PLpro domain), Nsp5 (Mpro, 3CLpro), Nsp12 (RdRp), Nsp15 (NendoU), N protein, and Spike. We performed VS experiments of FDA-approved drugs considering the therapeutic targets available at the web server to assess the impact of considering different structures and mutations to identify possible new treatments of SARS-CoV-2 infections. The DockThor-VS is freely available at www.dockthor.lncc.br .

New machine learning and physics-based scoring functions for drug discovery.

Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein-ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein-protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein-protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br .

Isobenzofuran-1(3H)-ones as new tyrosinase inhibitors: Biological activity and interaction studies by molecular docking and NMR.

Tyrosinase is a multifunctional, glycosylated and copper-containing oxidase enzyme that can be found in animals, plants, and fungi. It is involved in several biological processes such as melanin biosynthesis. In this work, a series of isobenzofuran-1(3H)-ones was evaluated as tyrosinase inhibitors. It was found that compounds phthalaldehydic acid (1), 3-(2,6-dihydroxy-4-isopropylphenyl)isobenzofuran-1(3H)-one (7), and 2-(3-oxo-1,3-dihydroisobenzofuran-1-yl)-1,3-phenylene diacetate (9) were the most potent compounds inhibiting tyrosinase activity in a concentration dependent manner. Ligand-enzyme NMR studies and docking investigations allowed to map the atoms of the ligands involved in the interaction with the copper atoms present in the active site of the tyrosinase. This behaviour is similar to kojic acid, a well know tyrosinase inhibitor and used as positive control in the biological assays. The findings herein described pave the way for future rational design of new tyrosinase inhibitors.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set.

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Discovery of naphthyl-N-acylhydrazone p38α MAPK inhibitors with in vivo anti-inflammatory and anti-TNF-α activity.

Protein kinases constitute attractive therapeutic targets for development of new prototypes to treat different chronic diseases. Several available drugs, like tinibs, are tyrosine kinase inhibitors; meanwhile, inhibitors of serine/threonine kinases, such as mitogen-activated protein kinase (MAPK), are still trying to overcome some problems in one of the steps of clinical development to become drugs. So, here we reported the synthesis, the in vitro kinase inhibitory profile, docking studies, and the evaluation of anti-inflammatory profile of new naphthyl-N-acylhydrazone derivatives using animal models. Although all tested compounds (3a-d) have been characterized as p38α MAPK inhibitors and have showed in vivo anti-inflammatory action, LASSBio-1824 (3b) presented the best performance as p38α MAPK inhibitor, with IC50  = 4.45 µm, and also demonstrated to be the most promising anti-inflammatory prototype, with good in vivo anti-TNF-α profile after oral administration.

Design, synthesis and evaluation of novel feruloyl-donepezil hybrids as potential multitarget drugs for the treatment of Alzheimer's disease.

A novel series of feruloyl-donepezil hybrid compounds were designed, synthesized and evaluated as multitarget drug candidates for the treatment of Alzheimer's Disease (AD). In vitro results revealed potent acetylcholinesterase (AChE) inhibitory activity for some of these compounds and all of them showed moderate antioxidant properties. Compounds 12a, 12b and 12c were the most potent AChE inhibitors, highlighting 12a with IC50 = 0.46 µM. In addition, these three most promising compounds exhibited significant in vivo anti-inflammatory activity in the mice paw edema, pleurisy and formalin-induced hyperalgesy models, in vitro metal chelator activity for Cu2+ and Fe2+, and neuroprotection of human neuronal cells against oxidative damage. Molecular docking studies corroborated the in vitro inhibitory mode of interaction of these active compounds on AChE. Based on these data, compound 12a was identified as a novel promising drug prototype candidate for the treatment of AD with innovative structural feature and multitarget effects.

LASSBio-1829 Hydrochloride: Development of a New Orally Active N-Acylhydrazone IKK2 Inhibitor with Anti-inflammatory Properties.

Inhibitor of nuclear factor κB kinase 2 (IKK2) is suggested to be a potential target for the development of novel anti-inflammatory and anticancer drugs. In this work, we applied structure-based drug design to improve the potency of the inhibitor (E)-N'-(4-nitrobenzylidene)-2-naphthohydrazide (LASSBio-1524, 1 a: IC50 =20 µm). The molecular model built for IKK2 together with the docking methodology employed were able to provide important and consistent information with respect to the structural and chemical inhibitor characteristics that may confer potency to IKK2 inhibitors, providing important guidelines for the development of a new N-acylhydrazone (NAH) derivative. (E)-N'-(4-(1H-pyrrolo[2,3-b]pyridin-4-yl)benzylidene)-2-naphthohydrazide hydrochloride (LASSBio-1829 hydrochloride, 10) is a 7-azaindole NAH able to inhibit IKK2 with an IC50 value of 3.8 µm. LASSBio-1829 hydrochloride was found to be active in several pharmacological inflammation tests in vivo, showing its potential as an anti-inflammatory prototype.

Receptor-ligand molecular docking.

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein-ligand applications. We summarise the main topics and recent computational and methodological advances in protein-ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.