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.

Synthesis and biological evaluation of 4-hydroxy-methylpiperidinyl-N-benzyl-acylarylhydrazone hybrids designed as novel multifunctional drug candidates for Alzheimer's disease.

The search for new drug candidates against Alzheimer's disease (AD) remains a complex challenge for medicinal chemists due to its multifactorial pathogenesis and incompletely understood physiopathology. In this context, we have explored the molecular hybridization of pharmacophore structural fragments from known bioactive molecules, aiming to obtain a novel molecular architecture in new chemical entities capable of concomitantly interacting with multiple targets in a so-called multi-target directed ligands (MTDLs) approach. This work describes the synthesis of 4-hydroxymethyl)piperidine-N-benzyl-acyl-hydrazone derivatives 5a-l, designed as novel MTDLs, showing improved multifunctional properties compared to the previously reported parent series of N-benzyl-(3-hydroxy)piperidine-acyl-hydrazone derivatives 4. The new improved derivatives were studied in silico, regarding their mode of interaction with AChE enzyme, and in vitro, for evaluation of their effects on the selective inhibition of cholinesterases, cellular antioxidant, and neuroprotective activities as their cytotoxicity in human neuroblastoma (SH-SY5Y) cells. Overall, compound PQM-181 (5 k) showed the best balanced selective and non-competitive inhibition of AChE (IC50 = 5.9 µM, SI > 5.1), with an additional antioxidant activity (IC50 = 7.45 µM) against neuronal t-BOOH-induced oxidative stress and neuroprotective ability against neurotoxicity elicited by both t-BOOH and OAß1-42, and a moderate ability to interfere in Aß1-42 aggregates, with low cytotoxicity and good predictive druggability properties, suggesting a multifunctional pharmacological profile suitable for further drug development against AD.

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.

Design, synthesis and pharmacological evaluation of N-benzyl-piperidinyl-aryl-acylhydrazone derivatives as donepezil hybrids: Discovery of novel multi-target anti-alzheimer prototype drug candidates.

A new series of sixteen multifunctional N-benzyl-piperidine-aryl-acylhydrazones hybrid derivatives was synthesized and evaluated for multi-target activities related to Alzheimer's disease (AD). The molecular hybridization approach was based on the combination, in a single molecule, of the pharmacophoric N-benzyl-piperidine subunit of donepezil, the substituted hydroxy-piperidine fragment of the AChE inhibitor LASSBio-767, and an acylhydrazone linker, a privileged structure present in a number of synthetic aryl- and aryl-acylhydrazone derivatives with significant AChE and anti-inflammatory activities. Among them, compounds 4c, 4d, 4g and 4j presented the best AChE inhibitory activities, but only compounds 4c and 4g exhibited concurrent anti-inflammatory activity in vitro and in vivo, against amyloid beta oligomer (AßO) induced neuroinflammation. Compound 4c also showed the best in vitro and in vivo neuroprotective effects against AßO-induced neurodegeneration. In addition, compound 4c showed a similar binding mode to donepezil in both acetylated and free forms of AChE enzyme in molecular docking studies and did not show relevant toxic effects on in vitro and in vivo assays, with good predicted ADME parameters in silico. Overall, all these results highlighted compound 4c as a promising and innovative multi-target drug prototype candidate for AD treatment.

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.

Structural modeling and docking studies of ribose 5-phosphate isomerase from Leishmania major and Homo sapiens: a comparative analysis for Leishmaniasis treatment.

Leishmaniases are caused by protozoa of the genus Leishmania and are considered the second-highest cause of death worldwide by parasitic infection. The drugs available for treatment in humans are becoming ineffective mainly due to parasite resistance; therefore, it is extremely important to develop a new chemotherapy against these parasites. A crucial aspect of drug design development is the identification and characterization of novel molecular targets. In this work, through an in silico comparative analysis between the genomes of Leishmania major and Homo sapiens, the enzyme ribose 5-phosphate isomerase (R5PI) was indicated as a promising molecular target. R5PI is an important enzyme that acts in the pentose phosphate pathway and catalyzes the interconversion of d-ribose-5-phosphate (R5P) and d-ribulose-5-phosphate (5RP). R5PI activity is found in two analogous groups of enzymes called RpiA (found in H. sapiens) and RpiB (found in L. major). Here, we present the first report of the three-dimensional (3D) structures and active sites of RpiB from L. major (LmRpiB) and RpiA from H. sapiens (HsRpiA). Three-dimensional models were constructed by applying a hybrid methodology that combines comparative and ab initio modeling techniques, and the active site was characterized based on docking studies of the substrates R5P (furanose and ring-opened forms) and 5RP. Our comparative analyses show that these proteins are structural analogs and that distinct residues participate in the interconversion of R5P and 5RP. We propose two distinct reaction mechanisms for the reversible isomerization of R5P to 5RP, which is catalyzed by LmRpiB and HsRpiA. We expect that the present results will be important in guiding future molecular modeling studies to develop new drugs that are specially designed to inhibit the parasitic form of the enzyme without significant effects on the human analog.

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.

Structural modelling and comparative analysis of homologous, analogous and specific proteins from Trypanosoma cruzi versus Homo sapiens: putative drug targets for chagas' disease treatment.

BACKGROUND: Trypanosoma cruzi is the etiological agent of Chagas' disease, an endemic infection that causes thousands of deaths every year in Latin America. Therapeutic options remain inefficient, demanding the search for new drugs and/or new molecular targets. Such efforts can focus on proteins that are specific to the parasite, but analogous enzymes and enzymes with a three-dimensional (3D) structure sufficiently different from the corresponding host proteins may represent equally interesting targets. In order to find these targets we used the workflows MHOLline and AnEnΠ obtaining 3D models from homologous, analogous and specific proteins of Trypanosoma cruzi versus Homo sapiens. RESULTS: We applied genome wide comparative modelling techniques to obtain 3D models for 3,286 predicted proteins of T. cruzi. In combination with comparative genome analysis to Homo sapiens, we were able to identify a subset of 397 enzyme sequences, of which 356 are homologous, 3 analogous and 38 specific to the parasite. CONCLUSIONS: In this work, we present a set of 397 enzyme models of T. cruzi that can constitute potential structure-based drug targets to be investigated for the development of new strategies to fight Chagas' disease. The strategies presented here support the concept of structural analysis in conjunction with protein functional analysis as an interesting computational methodology to detect potential targets for structure-based rational drug design. For example, 2,4-dienoyl-CoA reductase (EC 1.3.1.34) and triacylglycerol lipase (EC 3.1.1.3), classified as analogous proteins in relation to H. sapiens enzymes, were identified as new potential molecular targets.

Understanding the HIV-1 protease nelfinavir resistance mutation D30N in subtypes B and C through molecular dynamics simulations.

A major concern in the antiretroviral (ARV) treatment of HIV infections with protease inhibitors (PI) is the emergence of resistance, which results from the selection of distinct mutations within the viral protease (PR) gene. Among patients who do not respond to treatment with the PI nelfinavir (NFV), the D30N mutation is often observed. However, several reports have shown that D30N emerges with different frequencies in distinct HIV-1 genetic forms or subtypes. In the present work, we analyzed the binding of NFV and the Gag substrate CA/p2 to PR from HIV-1 subtypes B and C through molecular dynamics (MD) simulations. The wild-type and drug-resistant D30N mutants were investigated in both subtypes. The compensatory mutations N83T and N88D, observed in vitro and in vivo when subtype C acquires D30N, were also studied. D30N appears to facilitate conformational changes in subtype B PR, but not in that from subtype C, and this could be associated with disestablishment of an alpha-helical region of the PR. Furthermore, the total contact areas of NFV or the CA/p2 substrate with the mutant PR correlated with changes in the resistance patterns and replicative capacity. Finally, we observed in our MD simulations that mutant PR proteins show different patterns for hydrophobic/van der Waals contact. These findings suggest that different molecular mechanisms contribute to resistance, and we propose that a single mutation has distinct impacts on different HIV-1 subtypes.

A new approach for potential drug target discovery through in silico metabolic pathway analysis using Trypanosoma cruzi genome information

The current drug options for the treatment of chronic Chagas disease have not been sufficient and high hopes have been placed on the use of genomic data from the human parasite Trypanosoma cruzi to identify new drug targets and develop appropriate treatments for both acute and chronic Chagas disease. However, the lack of a complete assembly of the genomic sequence and the presence of many predicted proteins with unknown or unsure functions has hampered our complete view of the parasite's metabolic pathways. Moreover, pinpointing new drug targets has proven to be more complex than anticipated and has revealed large holes in our understanding of metabolic pathways and their integrated regulation, not only for this parasite, but for many other similar pathogens. Using an in silicocomparative study on pathway annotation and searching for analogous and specific enzymes, we have been able to predict a considerable number of additional enzymatic functions in T. cruzi. Here we focus on the energetic pathways, such as glycolysis, the pentose phosphate shunt, the Krebs cycle and lipid metabolism. We point out many enzymes that are analogous to those of the human host, which could be potential new therapeutic targets.

A new approach for potential drug target discovery through in silico metabolic pathway analysis using Trypanosoma cruzi genome information.

The current drug options for the treatment of chronic Chagas disease have not been sufficient and high hopes have been placed on the use of genomic data from the human parasite Trypanosoma cruzi to identify new drug targets and develop appropriate treatments for both acute and chronic Chagas disease. However, the lack of a complete assembly of the genomic sequence and the presence of many predicted proteins with unknown or unsure functions has hampered our complete view of the parasite's metabolic pathways. Moreover, pinpointing new drug targets has proven to be more complex than anticipated and has revealed large holes in our understanding of metabolic pathways and their integrated regulation, not only for this parasite, but for many other similar pathogens. Using an in silicocomparative study on pathway annotation and searching for analogous and specific enzymes, we have been able to predict a considerable number of additional enzymatic functions in T. cruzi. Here we focus on the energetic pathways, such as glycolysis, the pentose phosphate shunt, the Krebs cycle and lipid metabolism. We point out many enzymes that are analogous to those of the human host, which could be potential new therapeutic targets.

General methodology to optimize damping functions to account for charge penetration effects in electrostatic calculations using multicentered multipolar expansions.

We developed a methodology to optimize exponential damping functions to account for charge penetration effects when computing molecular electrostatic properties using the multicentered multipolar expansion method (MME). This methodology is based in the optimization of a damping parameter set using a two-step fast local fitting procedure and the ab initio (Hartree-Fock/6-31G** and 6-31G**+) electrostatic potential calculated in a set of concentric grid of points as reference. The principal aspect of the methodology is a first local fitting step which generates a focused initial guess to improve the performance of a simplex method avoiding the use of multiple runs and the choice of initial guesses. Three different strategies for the determination of optimized damping parameters were tested in the following studies: (1) investigation of the error in the calculation of the electrostatic interaction energy for five hydrogen-bonded dimers at standard and nonstandard hydrogen-bonded geometries and at nonequilibrium geometries; (2) calculation of the electrostatic molecular properties (potential and electric field) for eight small molecular systems (methanol, ammonia, water, formamide, dichloromethane, acetone, dimethyl sulfoxide, and acetonitrile) and for the 20 amino acids. Our results show that the methodology performs well not only for small molecules but also for relatively larger molecular systems. The analysis of the distinct parameter sets associated with different optimization strategies show that (i) a specific parameter set is more suitable and more general for electrostatic interaction energy calculations, with an average absolute error of 0.46 kcal/mol at hydrogen-bond geometries; (ii) a second parameter set is more suitable for electrostatic potential and electric field calculations at and outside the van der Waals (vdW) envelope, with an average error decrease >72% at the vdW surface. A more general amino acid damping parameter set was constructed from the original damping parameters derived for the small fragments and for the amino acids. This damping set is more insensitive to protein backbone and residue side-chain conformational changes and can be very useful for future docking and molecular dynamics protein simulations using ab initio based polarizable classical methods.

Molecular docking study and development of an empirical binding free energy model for phosphodiesterase 4 inhibitors.

In the present work, several computational methodologies were combined to develop a model for the prediction of PDE4B inhibitors' activity. The adequacy of applying the ligand docking approach, keeping the enzyme rigid, to the study of a series of PDE4 inhibitors was confirmed by a previous molecular dynamics analysis of the complete enzyme. An exhaustive docking procedure was performed to identify the most probable binding modes of the ligands to the enzyme, including the active site metal ions and the surrounding structural water molecules. The enzyme-inhibitor interaction enthalpies, refined by using the semiempirical molecular orbital approach, were combined with calculated solvation free energies and entropy considerations in an empirical free energy model that enabled the calculation of binding free energies that correlated very well with experimentally derived binding free energies. Our results indicate that both the inclusion of the structural water molecules close to the ions in the binding site and the use of a free energy model with a quadratic dependency on the ligand free energy of solvation are important aspects to be considered for molecular docking investigations involving the PDE4 enzyme family.

A genetic algorithm for the ligand-protein docking problem

We analyzed the performance of a real coded "steady-state" genetic algorithm (SSGA) using a grid-based methodology in docking five HIV-1 protease-ligand complexes having known three-dimensional structures. All ligands tested are highly flexible, having more than 10 conformational degrees of freedom. The SSGA was tested for the rigid and flexible ligand docking cases. The implemented genetic algorithm was able to dock successfully rigid and flexible ligand molecules, but with a decreasing performance when the number of ligand conformational degrees of freedom increased. The docked lowest-energy structures have root mean square deviation (RMSD) with respect to the corresponding experimental crystallographic structure ranging from 0.037 Å to 0.090 Å in the rigid docking, and 0.420 Å to 1.943 Å in the flexible docking. We found that not only the number of ligand conformational degrees of freedom is an important aspect to the algorithm performance, but also that the more internal dihedral angles are critical. Furthermore, our results showed that the initial population distribution can be relevant for the algorithm performance.