Combining ligand-based and structure-based drug design in the virtual screening arena

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods

ABC transporters play a critical role in both drug bioavailability and toxicity, and with the discovery of the P-glycoprotein (P-gp), this became even more evident, as it plays an important role in preventing intracellular accumulation of toxic compounds. Over the past 30 years, intensive studies have been conducted to find new therapeutic molecules to reverse the phenomenon of multidrug resistance (MDR) ), that research has found is often associated with overexpression of P-gp, the most extensively studied drug efflux transporter; in MDR, therapeutic drugs are prevented from reaching their targets due to active efflux from the cell. The development of P-gp inhibitors is recognized as a good way to reverse this type of MDR, which has been the subject of extensive studies over the past few decades. Despite the progress made, no effective P-gp inhibitors to reverse multidrug resistance are yet on the market, mainly because of their toxic effects. Computational studies can accelerate this process, and in silico models such as QSAR models that predict the activity of compounds associated with P-gp (or analogous transporters) are of great value in the early stages of drug development, along with molecular modelling methods, which provide a way to explain how these molecules interact with the ABC transporter. This review highlights recent advances in computational P-gp research, spanning the last five years to 2022. Particular attention is given to the use of machine-learning approaches, drug-transporter interactions, and recent discoveries of potential P-gp inhibitors that could act as modulators of multidrug resistance.

ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery

An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new graph-attention-based approach, is proposed to improve the accuracy of molecular representation by simultaneously considering atom-atom, bond-bond and atom-bond interactions. In addition, we benchmark models extensively on 8 public and 680 proprietary industrial datasets spanning a wide variety of chemical end points. The results show that ComABAN has higher prediction accuracy compared with the classical machine learning method and the deep learning-based methods. Furthermore, the trained neural network was used to predict a library of 1.5 million molecules and picked out compounds with a classification result of grade I. Subsequently, these predicted molecules were scored and ranked using cascade docking, molecular dynamics simulations to generate five potential candidates. All five molecules showed high similarity to nanomolar bioactive inhibitors suppressing the expression of HIF-1α, and we synthesized three compounds (Y-1, Y-3, Y-4) and tested their inhibitory ability in vitro. Our results indicate that ComABAN is an effective tool for accelerating drug discovery.

Developing an effective polarizable bond method for small molecules with application to optimized molecular docking

Electrostatic interaction plays an essential role in protein-ligand binding. Due to the polarization effect, electrostatic interactions are largely impacted by their local environments. However, traditional force fields use fixed point charge-charge interactions to describe electrostatic interactions but is unable to include the polarization effect. The lack of the polarization effect in the force field representation can result in substantial error in biomolecular studies, such as molecular dynamics and molecular docking. Docking programs usually employ traditional force fields to estimate the binding energy between a ligand and a protein for pose selection or scoring. The intermolecular interaction energy mainly consists of van der Waals and electrostatic interaction in the force field representation. In the current study, we developed an Effective Polarizable Bond (EPB) method for small organic molecules and applied this EPB method to optimize protein-ligand docking in computational tests for a variety of protein-ligand systems. We tested the method on a set of 38 cocrystallized structures taken from the Protein Data Bank (PDB) and found that the maximum error was reduced from 7.98 Å to 2.03 Å when using EPB Dock, providing strong evidence that the use of EPB charges is important. We found that our optimized docking approach with EPB charges could improve the docking performance, sometimes dramatically, and the maximum error was reduced from 12.88 Å to 1.57 Å in Optimized Docking (in the case of 1fqx). The average RMSD decreased from 2.83 Å to 1.85 Å. Further investigations showed that the use of the EBP method could enhance intermolecular hydrogen bonding, which is a major contributing factor to improved docking performance. Developed tools for the calculation of the polarized ligand charge from a protein-ligand complex structure with the EPB method are freely available on GitHub (https://github.com/Xundrug/EPB).

Three Dimensional Quantitative Structure Activity Relationship and Pharmacophore Modeling of Tacrine Derivatives as Acetylcholinesterase Inhibitors in Alzheimer's Treatment

Background:

Three dimensional quantitative structure activity relationship and pharmacophore modeling are studied for tacrine derivatives as acetylcholinesterase inhibitors.

Methods:

The three dimensional quantitative structure–activity relationship and pharmacophore methods were used to model the 68 derivatives of tacrine as human acetylcholinesterase inhibitors. The effect of the docked conformer of each molecule in the enzyme cavity was investigated on the predictive ability and statistical quality of the produced models.

Results:

The whole data set was divided into two training and test sets using hierarchical clustering method. 3D-QSAR model, based on the comparative molecular field analysis has good statistical parameters as indicated by q2 =0.613, r2 =0.876, and r2pred =0.75. In the case of comparative molecular similarity index analysis, q2, r2 and r2pred values were 0.807, 0.96, and 0.865 respectively. The statistical parameters of the models proved that the inhibition data are well fitted and they have satisfactory predictive abilities.

Conclusion :

The results from this study illustrate the reliability of using techniques in exploring the likely bonded conformations of the ligands in the active site of the protein target and improve the understanding over the structural and chemical features of AChE.

Designing a ligand for pharmaceutical purposes

BACKGROUND

Drug approval applications to the FDA have shown a remarkably small increment compared with what was expected. In the last few years several efforts have been made to improve the results of rational drug design approaches and in particular to predict inhibitor-target structure and to evaluate the free energy of binding. Virtual database screening, combined with other computational methods, is one of the most promising methods to overcome this key issue.

OBJECTIVE

It is possible to understand how computational medicinal chemistry is changing, improving from its errors and moving towards becoming a more important tool for drug development.

METHODS

Some of the most recent modeling techniques have been presented and in particular the benefits of combining these techniques are highlighted.

RESULTS/CONCLUSION

At present computational chemists can understand the peculiar problems associated with the study of biological systems and on this basis they can choose the right collection of complementary in silico approaches to solve the medicinal chemistry problem in a better manner.

Discovery of novel small-molecule inhibitors of human epidermal growth factor receptor-2: combined ligand and target-based approach

Consensus virtual screening models were generated and validated utilizing a set of known human epidermal growth factor receptor-2 (HER2) inhibitors and modeled HER2 active and inactive state structures. The virtual screening models were successfully employed to discover a set of structurally diverse compounds with growth inhibitory activity against HER2-overexpressing SKBR3 breast cancer cell line. A search of a 3D database containing 350000 small-molecules using the consensus models retrieved 531 potential hits. Of the 531 hits, 57 were selected for testing in SKBR3 cells on the basis of structural novelty and desirable drug-like properties. Seven compounds inhibited growth of SKBR3 cells with IC50 values <10 microM. These lead compounds have desirable physicochemical properties and are excellent candidates for further optimization.

Linear and nonlinear 3D-QSAR approaches in tandem with ligand-based homology modeling as a computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine A2A receptor

The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rhodopsin-driven homology modeling approach, we have built a model of the human adenosine A2A receptor. Finally, 3D-QSAR and LBHM strategies have been utilized to predict the binding affinity of five new human A2A pyrazolo-triazolo-pyrimidine antagonists finding a good agreement between the theoretical and the experimental predictions.

Tandem 3D-QSARs approach as a valuable tool to predict binding affinity data: design of new Gly/NMDA receptor antagonists as a key study

Quantitative structure-activity relationships (QSARs) represent a very well consolidated computational approach to correlate structural or property descriptors of chemical compounds with their chemical or biological activities. We have recently reported that autocorrelation Molecular Electrostatic Potential (autoMEP) vectors in combination to Partial Least-Square (PLS) analysis or to Response Surface Analysis (RSA) can represent an interesting alternative 3D-QSAR strategy. In the present paper, we would like to present how the applicability of in tandem linear and nonlinear 3D-QSAR methods (autoMEP/PLS&RSA) can help to predict binding affinity data of a new set of N-methyl-d-aspartate (Gly/NMDA) receptor antagonists.