Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.

Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments

Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

Drug repurposing: progress, challenges and recommendations

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

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Computational Methods in Drug Discovery

Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.

Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

Halogen bonding has been known in material science for decades, but until recently, halogen bonds in protein–ligand interactions were largely the result of serendipitous discovery rather than rational design. In this Perspective, we provide insights into the phenomenon of halogen bonding, with special focus on its role in drug discovery. We summarize the theoretical background defining its strength and directionality, provide a systematic analysis of its occurrence and interaction geometries in protein–ligand complexes, and give recent examples where halogen bonding has been successfully harnessed for lead identification and optimization. In light of these data, we discuss the potential and limitations of exploiting halogen bonds for molecular recognition and rational drug design.

Principles and applications of halogen bonding in medicinal chemistry and chemical biology.

Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.

A critical assessment of docking programs and scoring functions

PI3Kδ is a lipid kinase which plays a key role in airway inflammatory conditions. Accordingly, the inhibition of PI3Kδ can be considered a valuable strategy for the treatment of chronic respiratory diseases such as Asthma and Chronic obstructive pulmonary disease (COPD). In this work, we describe our efforts to identify new PI3Kδ inhibitors following an “inhalation by design” strategy. Starting from the identification of a purine scaffold, we carried out a preliminary SAR expansion which led to the identification of a new hit characterized by a high enzymatic potency and moderate PI3Kδ selectivity. A subsequent optimization led to novel purine based derivatives with favorable in vitro ADME profiles, which might represent promising starting points for future development of new inhaled drug candidates. 

Application of an "inhalation by design" approach to the identification and in-vitro evaluation of novel purine based PI3Kδ inhibitors.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease characterized by lung fibrosis leading to an irreversible decline of lung function. Current antifibrotic drugs on the market slow down but do not prevent the progression of the disease and are associated with tolerability issues. The involvement of lysophosphatidic acid receptor 2 (LPA2) in IPF is supported by LPA2 knockdown studies. To further validate the role of LPA2 receptors in modulating IPF and potentially other fibrotic processes, a potent and selective LPA2 receptor antagonist with a good pharmacokinetic (PK) profile is needed. Herein, we report the medicinal chemistry exploration that led to the discovery of a new class of highly potent and selective LPA2 antagonists. Among them, compound 58 exhibits excellent potency, selectivity, and oral PK profile, making it a suitable tool for probing the involvement of LPA2 receptors in IPF and other fibrotic processes.

Discovery of a Potent, Selective, and Orally Bioavailable Tool Compound for Probing the Role of Lysophosphatidic Acid Type 2 Receptor Antagonists in Fibrotic Disorders.

Anna-Maria Capelli

Papers

A critical assessment of docking programs and scoring functions

Application of an "inhalation by design" approach to the identification and in-vitro evaluation of novel purine based PI3Kδ inhibitors.

Discovery of a Potent, Selective, and Orally Bioavailable Tool Compound for Probing the Role of Lysophosphatidic Acid Type 2 Receptor Antagonists in Fibrotic Disorders.