Computational methods in drug discovery.

High throughput in silico methods have offered the tantalizing potential to drastically accelerate the drug discovery process. Yet despite significant efforts expended by academia, national labs and industry over the years, many of these methods have not lived up to their initial promise of reducing the time and costs associated with the drug discovery enterprise, a process that can typically take over a decade and cost hundreds of millions of dollars from conception to final approval and marketing of a drug. Nevertheless structure-based modeling has become a mainstay of computational biology and medicinal chemistry, helping to leverage our knowledge of the biological target and the chemistry of protein-ligand interactions. While ligand-based methods utilize the chemistry of molecules that are known to bind to the biological target, structure-based drug design methods rely on knowledge of the three-dimensional structure of the target, as obtained through crystallographic, spectroscopic or bioinformatics techniques. Here we review recent developments in the methodology and applications of structure-based and ligand-based methods and target-based chemogenomics in Virtual High Throughput Screening (VHTS), highlighting some case studies of recent applications, as well as current research in further development of these methods. The limitations of these approaches will also be discussed, to give the reader an indication of what might be expected in years to come.

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B ligand-based and structure-based computational methods are invaluable tools in computer aided drug discovery. This presentation will introduce technological advancements in both disciplines. First, we introduce BCL: ChemInfo, a comprehensive machine learning-based quantitative structure activity relationship (QSAR) modeling framework featuring novel molecular descriptors, diverse automated feature selection, GPU acceleration, and consensus model analysis. Public availability of high-throughput screening (HTS) data is rapidly increasing highlighting the need for ligand-based computational methods, such as BCL: ChemInfo, to accelerate probe development and drug discovery while reducing costs. The framework was benchmarked on publically available HTS data (PubChem) for nine targets, selected as representatives for the major protein families most commonly targeted by therapeutics, to investigate the influence of size and composition of training data, the choice of objective function, effectiveness of several feature selection techniques, as well as the predictive power of consensus predictors using different machine learning techniques. Second, recently advancements in the small molecule docking using RosettaLigand will be presented including simultaneous docking of explicit interface water molecules, and small molecule docking into comparative models. A study performed on protein-centric water docking shows an improvement in ligand placement at a ratio of 9:1 while ligand-centric water docking allows for the recovery of up to 56% of failed docking studies using 341 structures from the CSAR benchmark of diverse protein-ligand complexes. A study on docking into comparative models found RosettaLigand was successful in recovering a native-like binding mode among the top ten scoring binding modes for 21 of 30 cases while template selection based on ligand occupancy rather than template-target identity was discovered to increase success.

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 efforts 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.

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch.

Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .

Monoclonal antibody inhibitors targeting PD-1/PD-L1 immune checkpoints are gradually entering the market and have achieved certain positive effects in the treatments of various types of tumors. However, with the expansion of application, the limitations of antibody drugs and problems such as excessive homogenization of research gradually appear, making small-molecule inhibitors the new focus of researchers. This study aims to use ligand-based and structure-based binding activity prediction methods to achieve virtual screening of small-molecule inhibitors targeting PD-L1, thereby helping to accelerate the development of small molecule drugs. A dataset of PD-L1 small-molecule inhibitory activity from relevant research literature and patents is collected and activity judgment classification models with intensity prediction regression models are constructed based on different molecular featurization methods and machine learning algorithms. The two types of models filter 68 candidate compounds with high PD-L1 inhibitory activity from a large drug-like small molecule screening pool (ZINC15). Ten of these compounds not only have good drug similarities and pharmacokinetics, but also exhibit comparable binding affinities and similar mechanisms of action with previous reported hotspot compounds in molecular docking. This phenomenon is further verified in subsequent molecular dynamics simulation and the estimation of binding free energy. In this study, a virtual screening workflow integrating ligand-based method and structure-based method is developed, and potential PD-L1 small-molecule inhibitors are effectively screened from large compound databases, which is expected to help accelerate the application and expansion of tumor immunotherapy.

Quantitative structure–activity relationships (QSARs) are mathematical equations or other types of functions (such as the weights of connections in artificial neural networks) relating chemical structures to their biological activity. The purpose of QSAR studies is to predict novel structures with either beneficial activity as drugs for human or veterinary medicine (bactericides, antiviral or anticancer drugs, and metabolic regulators, such as hypoglycemics, hypotensives, etc.), or selective toxicity for various unwanted higher organisms (pesticides such as fungicides, insecticides, acaricides, weed killers, etc.) Quantitative structure–property relationships (QSPRs) are similar, but the property may be physical or chemical. The main problems involvefinding a mathematical representation of chemical structures (usually organic molecules), represented either as molecular graphs by their constitution or connectivity without considering the three‐dimensional (3‐D) (stereochemical) factors, or as 3‐D objects including stereochemical information;measuring the biological activities of a series of molecules;finding the QSAR between each type of biological activity and the most convenient molecular descriptors.Physicochemical parameters that are closely related to drug transport and ligand binding include lipophilicity, polarity, polarizability, and electronic influence on hydrogen‐donor and hydrogen‐acceptor binding. The next task is to use the QSAR to predict which novel structures to prepare in order to obtain molecules with the desired biological activity. Then the cycle is usually repeated, because a single pass seldom affords the optimal solution.The main mathematical descriptors and the techniques for obtaining QSARs are reviewed. An important molecular parameter, which may be measured experimentally or computed from the chemical structure, is lipophilicity or hydrophobicity; Hansch introduced then‐octanol–water partition coefficient as a measure of this property, which, to a large extent, determines the ability of molecules to penetrate the lipophilic, bilayer, extra‐ or intracellular membranes. Lipophilicity levels that are too high lead to insolubility in water, i.e. it is difficult to administer the drug orally via the digestive tract. The main constitutional and 3‐D molecular descriptors are described and examples are given. To be statistically valid, correlations must involve orthogonal or orthogonalized descriptors. Linear, multilinear, and nonlinear types of correlations are reviewed. Screening of virtual combinatorial libraries together with high‐throughput combinatorial synthesis and testing provides modern tools for more efficient drug design.

Quantitative structure–activity relationships (QSARs) are mathematical equations or other types of functions (such as the weights of connections in artificial neural networks) relating chemical structures to their biological activity. The purpose of QSAR studies is to predict novel structures with either beneficial activity as drugs for human or veterinary medicine (bactericides, antiviral or anticancer drugs, metabolic regulators such as hypoglycemics, hypotensives, etc.), or selective toxicity for various unwanted higher organisms (pesticides such as fungicides, insecticides, acaricides, weed‐killers, etc.). Quantitative structure–property relationships (QSPRs) are similar, but the property may be physical or chemical. The main problems involve:finding a mathematical representation of chemical structures (usually organic molecules), represented either as molecular graphs by their constitution or connectivity without considering the three‐dimensional (3‐D) (stereochemical) factors, or as 3‐D objects including stereochemical information;measuring the biological activities of a series of molecules;finding the QSAR between each type of biological activity and the most convenient molecular descriptors.Physicochemical parameters that are closely related to drug transport and ligand binding include lipophilicity, polarity, polarizability, and electronic influence on hydrogen‐donor and hydrogen‐acceptor binding. The next task is to use the QSAR to predict which novel structures to prepare in order to obtain molecules with the desired biological activity. Then the cycle is usually repeated, because a single pass seldom affords the optimal solution.The main mathematical descriptors and the techniques for obtaining QSARs are reviewed. An important molecular parameter, which may be measured experimentally or computed from the chemical structure, is lipophilicity or hydrophobicity; Hansch (see below) introduced the n‐octanol–water partition coefficient as a measure of this property, which to a large extent determines the ability of molecules to penetrate the lipophilic, bilayer, extra‐ or intracellular membranes. Lipophilicity levels that are too high lead to insolubility in water, i.e. it is difficult to administer the drug orally via the digestive tract. The main constitutional and 3‐D molecular descriptors are described and examples are given. To be statistically valid, correlations must involve orthogonal or orthogonalized descriptors. Linear, multilinear, and nonlinear types of correlations are reviewed. Screening of virtual combinatorial libraries together with high‐throughput combinatorial synthesis and testing provide modern tools for more efficient drug design.

Virtual screening (VS) can be accomplished in either ligand- or structure-based methods. In recent times, an increasing number of 2D fingerprint and 3D shape similarity methods have been used in ligand-based VS. To evaluate the performance of these ligand-based methods, retrospective VS was performed on a tailored directory of useful decoys (DUD). The VS performances of 14 2D fingerprints and four 3D shape similarity methods were compared. The results revealed that 2D fingerprints ECFP_2 and FCFP_4 yielded better performance than the 3D Phase Shape methods. These ligand-based methods were also compared with structure-based methods, such as Glide docking and Prime molecular mechanics generalized Born surface area rescoring, which demonstrated that both 2D fingerprint and 3D shape similarity methods could yield higher enrichment during early retrieval of active compounds. The results demonstrated the superiority of ligand-based methods over the docking-based screening in terms of both speed and hit enrichment. Therefore, considering ligand-based methods first in any VS workflow would be a wise option.