CombiDOCK: structure-based combinatorial docking and library design

Emerging structure-based computational methods to screen the exploding accessible chemical space

Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hitting high-quality compounds during virtual screening exercises but also poses new challenges as the number of chemically accessible molecules grows faster than the computing power necessary to screen them. We review here two novel approaches rapidly gaining in popularity to address this problem: machine learning-accelerated and synthon-based library screening. We summarize the results from seminal proof-of-concept studies, highlight the latest developments, and discuss limitations and future directions.

The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

Screening Library Design

Thanks to technological advances and a greater understanding of the biological and chemical natures of targets and related diseases, high-throughput screening (HTS) has been allowed to be faster, cheaper, and more accessible. Yet, despite these increased technologies and understandings, the frequency of novel and drugs are being approved each year has not being increasing over the years. 2017 was considered a "bumper" year with a total of 46 approved drugs, over double that of the previous year. However, it is thought that part of the problem that HTS has not lived up to expectations is because of the contents of current chemical libraries. Therefore, new methods to design screening libraries are of great interest.

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Fragment-based approaches and computer-aided drug discovery

Fragment-based design has significantly modified drug discovery strategies and paradigms in the last decade. Besides technological advances and novel therapeutic avenues, one of the most significant changes brought by this new discipline has occurred in the minds of drug designers. Fragment-based approaches have markedly impacted rational computer-aided design both in method development and in applications. The present review illustrates the importance of molecular fragments in many aspects of rational ligand design, and discusses how thinking in "fragment space" has boosted computational biology and chemistry.

Methods for combinatorial and parallel library design

Diversity has historically played a critical role in design of combinatorial libraries, screening sets and corporate collections for lead discovery. Large library design dominated the field in the 1990s with methods ranging anywhere from purely arbitrary through property based reagent selection to product based approaches. In recent years, however, there has been a downward trend in library size. This was due to increased information about the desirable targets gleaned from the genomics revolution and to the ever growing availability of target protein structures from crystallography and homology modeling. Creation of libraries directed toward families of receptors such as GPCRs, kinases, nuclear hormone receptors, proteases, etc., replaced the generation of libraries based primarily on diversity while single target focused library design has remained an important objective. Concurrently, computing grids and cpu clusters have facilitated the development of structure based tools that screen hundreds of thousands of molecules. Smaller "smarter" combinatorial and focused parallel libraries replaced those early un-focused large libraries in the twenty-first century drug design paradigm. While diversity still plays a role in lead discovery, the focus of current library design methods has shifted to receptor based methods, scaffold hopping/bio-isostere searching, and a much needed emphasis on synthetic feasibility. Methods such as "privileged substructures based design" and pharmacophore based design still are important methods for parallel and small combinatorial library design. This chapter discusses some of the possible design methods and presents examples where they are available.

Docking methods for structure-based library design

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

Conformational dynamics and ligand binding in the multi-domain protein PDC109

PDC109 is a modular multi-domain protein with two fibronectin type II (Fn2) repeats joined by a linker. It plays a major role in bull sperm binding to the oviductal epithelium through its interactions with phosphorylcholines (PhCs), a head group of sperm cell membrane lipids. The crystal structure of the PDC109-PhC complex shows that each PhC binds to the corresponding Fn2 domain, while the two domains are on the same face of the protein. Long timescale explicit solvent molecular dynamics (MD) simulations of PDC109, in the presence and absence of PhC, suggest that PhC binding strongly correlates with the relative orientation of choline-phospholipid binding sites of the two Fn2 domains; unless the two domains tightly bind PhCs, they tend to change their relative orientation by deforming the flexible linker. The effective PDC109-PhC association constant of 28 M(-1), estimated from their potential of mean force is consistent with the experimental result. Principal component analysis of the long timescale MD simulations was compared to the significantly less expensive normal mode analysis of minimized structures. The comparison indicates that difference between relative domain motions of PDC109 with bound and unbound PhC is captured by the first principal component in the principal component analysis as well as the three lowest normal modes in the normal mode analysis. The present study illustrates the use of detailed MD simulations to clarify the energetics of specific ligand-domain interactions revealed by a static crystallographic model, as well as their influence on relative domain motions in a multi-domain protein.

Structure-based approaches to antibiotic drug discovery

The development of antimicrobials has advanced tremendously over the past century. However, as our production capacity increases, the threat of resistance is ever-present. To combat this resistance, two main avenues of drug discovery are being pursued: identifying new microbial proteins for which to direct drug discovery efforts, and designing innovative drugs that target existing proteins. The advent of structural genomics research has advanced to the point of rapidly discovering novel microbial protein targets. In addition, modern tools of computational biology greatly enhance the speed and reliability of antimicrobial discovery. The various steps of this process are outlined and discussed, including virtual ligand screening, pocket identification, and compound optimization.

Fully automated molecular mechanics based induced fit protein-ligand docking method

We describe a method for docking a ligand into a protein receptor while allowing flexibility of the protein binding site. The method employs a multistep procedure that begins with the generation of protein and ligand conformations. An initial placement of the ligand is then performed by computing binding site hotspots. This initial placement is followed by a protein side-chain refinement stage that models protein flexibility. The final step of the process is an energy minimization of the ligand pose in the presence of the rigid receptor. Thus the algorithm models flexibility of the protein at two stages, before and after ligand placement. We validated this method by performing docking and cross docking studies of eight protein systems for which crystal structures were available for at least two bound ligands. The resulting rmsd values of the 21 docked protein-ligand complexes showed values of 2 A or less for all but one of the systems examined. The method has two critical benefits for high throughput virtual screening studies. First, no user intervention is required in the docking once the initial binding site selection has been made in the protein. Second, the initial protein conformation generation needs to be performed only once for a given binding region. Also, the method may be customized in various ways depending on the particular scenario in which dockings are being performed. Each of the individual steps of the method is fully independent making it straightforward to explore different variants of the high level workflow to further improve accuracy and performance.

Structure-directed combinatorial library design

In recent years pharmaceutical companies have utilized structure-based drug design and combinatorial library design techniques to speed up their drug discovery efforts. Both approaches are routinely used in the lead discovery and lead optimization stages of the drug discovery process. Fragment-based drug design, a new power tool in the drug design toolbox, is also gaining acceptance across the pharmaceutical industry. This review will focus on the interplay between these three design techniques and recent developments in computational methodologies that enhance their integration. Examples of successful synergistic applications of these three techniques will be highlighted. Opinion regarding possible future directions of the field will be given.

TrixX: structure-based molecule indexing for large-scale virtual screening in sublinear time

Structure-based virtual screening today is basically organized as a sequential process where the molecules of a screening library are evaluated for instance with respect to their fit with a biological target. In this paper, we present a novel structure-based screening paradigm avoiding sequential searching and therefore enabling sublinear runtime behavior. We implemented the novel paradigm in the virtual screening tool TrixX and successfully applied it in screening experiments on four targets from relevant therapeutic areas. With the screening paradigm implemented in TrixX, we propose some important extensions and modifications to traditional virtual screening approaches: Instead of processing all compounds in the screening library sequentially, TrixX first analyzes the geometric and physicochemical binding site characteristics and then draws compounds with matching features from a compound catalog. The catalog organizes the compounds by their physicochemical and geometric features making use of relational database technology with indexed tables in order to support efficient queries for compounds with specific features. A key element of the compound catalog is a highly selective geometric descriptor that carries information on the type of functional groups of the compound, their Euclidian distance, the preferred interaction direction of each functional group, and the location of steric bulk around the triangle. In a re-docking experiment with 200 protein-ligand complexes, we could show that TrixX is able to correctly predict the location of ligand functional groups in co-crystallized complexes. In a retrospective virtual screening experiment for four different targets, the enrichment factors of TrixX are comparable to the enrichment factors of FlexX and FlexX-Scan. With computing times clearly below one second per compound, TrixX counts among the fastest virtual screening tools currently available and is nearly two orders of magnitude faster than standard FlexX.

Comparison of some 3-(substituted-benzylidene)-1, 3-dihydro-indolin derivatives as ligands of tyrosine kinase based on binding mode studies and biological assay

A series of 3-(substituted-benylidene)-1, 3-dihydro- indolin-2-one, 3-(substituted-benylidene)-1, 3-dihydro- indolin-2-thione and 2, 2'-dithiobis 3-(substituted-benylidene)-1, 3-dihydro-indole derivatives was investigated as inhibitor of p60c-Src tyrosine kinase by performing receptor docking studies and inhibitory activity toward tyrosine phosphorylation. Some compounds were shown to be docked at the site, where the selective inhibitor PP1 [1-tert-Butyl-3-p-tolyl-1H-pyrazolo[3,4-d]pyrimidine-4-yl-amine] was embedded at the enzyme active site. Evaluation of all compounds for the interactions with the parameters of lowest binding energy levels, capability of hydrogen bond formations and superimposibility on enzyme active site by docking studies, it can be assumed that 3-(substituted- benzylidene)-1, 3-dihydro-indolin-2-one and thione derivatives have better interaction with enzyme active site then 2, 2'-dithiobis 3-(substituted-benzylidene)-1, 3-dihydro indole derivatives. The test results for the inhibitory activity against tyrosine kinase by Elisa method revealed that 3-(substituted-benylidene)-1, 3-dihydro- indolin-2-thione derivatives have more activity then 3-(substituted-benylidene)-1, 3-dihydro- indolin-2-one derivatives.

4SCan/vADME: intelligent library screening as a shortcut from hits to lead compounds

Managing to solve the first step in drug discovery - the hit finding - can be a quite elaborate task, but it is only the initial step to the final goal; hit-to-lead optimisation still lies ahead and consumes even more time and resources. The solution is rather simple, that is, to take only the most promising compounds into account; but who is going to decide which ones are the most promising among a list of tens of millions of compounds in a virtual combinatorial library? 4SCan/vADME helps by bridging the gap between virtual (combinatorial) libraries designed by chemists and the in silico methods, docking and alignment, for screening databases. After choosing a random starting set, the implemented learning and prediction algorithm iteratively considers only combinations of fragments that have shown to result in more suitable interactions by the chosen method. ADME properties of the final list are then calculated via several in silico methods, resulting in a combined evaluation of the individual compound's target-specific, as well as ADME, properties. Based on the latter list of evaluated compounds, medicinal chemists can then decide which compounds might be the best ones to synthesise first and to serve as possible lead candidates. Following a brief introduction to virtual high-throughput screening techniques, the 4SCan/vADME method is outlined and discussed in this paper, using an example coming out of the 4SC pipeline.

Surrogate docking: structure-based virtual screening at high throughput speed

Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing approximately 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size - not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of approximately 13 and approximately 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.

FlexX-Scan: fast, structure-based virtual screening

We present a new software module, FlexX-Scan, for high-throughput, structure-based virtual screening. FlexX-Scan was developed with the aim to further speed up the virtual screening process. Based on the incremental construction docking tool FlexX (Rarey et al., J Mol Biol 1996;261:470-489), a compact descriptor for representing favorable protein interaction spots within the protein binding site has been developed. The descriptor is calculated using special-purpose clustering techniques applied to the usual interaction points created by FlexX. The algorithm automatically detects a small set of interaction spots in the binding site for positioning ligand functional groups. The parametrizations of the base placement and incremental construction algorithms have been adapted to the new interaction model. We tested the software tool on a diverse set of 200 protein-ligand complexes from the protein database (PDB) (Kramer et al., Proteins 1999;37:228-241). On average, the algorithm proposes about 90 interaction spots per binding site compared to about 1000 interaction dots in FlexX. We observe that the docking solutions of FlexX-Scan have a root-mean-square deviation from the crystal structure similar to the deviation of docking solutions of standard FlexX. For further validation we also performed virtual screening experiments for cyclin-dependent kinase 2, thrombin, angiotensin-converting enzyme, and dihydrofolat reductase. In these experiments, we screened a set of 34,000 random compounds and a number of known actives for each target. With FlexX-Scan, we achieved comparable enrichments to standard FlexX, with an averaged computing time of 5-10 s per compound, depending on parametrization.

A very large diversity space of synthetically accessible compounds for use with drug design programs

We have constructed a very large virtual diversity space containing more than 10(13) chemical compounds. The diversity space is built from about 400 combinatorial libraries, which have been expanded by choosing sizeable collections of suitable R-groups that can be attached to each link point of their scaffolds. These R-group collections have been created by selecting reagents that have drug-like properties from catalogs of available chemicals. As members of known combinatorial libraries, the compounds in the diversity space are in general synthetically accessible and useful as potential drug leads. Hence, the diversity space can be used as a vast source of compounds by a de novo drug design program. For example, we have used such a program to generate inhibitors of HIV integrase enzyme that exhibited activity in the micromolar range.

Identifying the binding mode of a molecular scaffold

We describe a method for docking of a scaffold-based series and present its advantages over docking of individual ligands, for determining the binding mode of a molecular scaffold in a binding site. The method has been applied to eight different scaffolds of protein kinase inhibitors (PKI). A single analog of each of these eight scaffolds was previously crystallized with different protein kinases. We have used FlexX to dock a set of molecules that share the same scaffold, rather than docking a single molecule. The main mode of binding is determined by the mode of binding of the largest cluster among the docked molecules that share a scaffold. Clustering is based on our 'nearest single neighbor' method [J. Chem. Inf. Comput. Sci., 43 (2003) 208-217]. Additional criteria are applied in those cases in which more than one significant binding mode is found. Using the proposed method, most of the crystallographic binding modes of these scaffolds were reconstructed. Alternative modes, that have not been detected yet by experiments, could also be identified. The method was applied to predict the binding mode of an additional molecular scaffold that was not yet reported and the predicted binding mode has been found to be very similar to experimental results for a closely related scaffold. We suggest that this approach be used as a virtual screening tool for scaffold-based design processes.

Virtual screening of virtual libraries

Virtual screening of virtual libraries (VSVL) is a rapidly changing area of research. Great efforts are being made to produce better algorithms, selection methods and infrastructure. Yet, the number of successful examples in the literature is not impressive, although the quality of work certainly is high. Why is this? One reason is that these methods tend to be applied at the lead generation stage and therefore there is a large lead-time before successful examples appear in the literature. However, any computational chemist would confirm that these methods are successful and there exists a glut of start-up companies specialising in virtual screening. Moreover, the scientific community would not be focussing so much attention on this area if it were not yielding results. Even so, the paucity of literature data is certainly a hindrance to the development of better methods. The VSVL process is unique within the discovery process, in that it is the only method that can screen the > 10(30) genuinely novel molecules out there. Already, some VSVL methods are evaluating 10(13) compounds, a capacity that high throughput screening can only dream of. There is a huge potential advantage for the company that develops efficient and effective methods, for lead generation, lead hopping and optimization of both potency and ADME properties. To do this, it requires more than the software, it requires confidence to exploit the methodology, to commit synthesis on the basis of it, and to build this approach into the medicinal chemistry strategy. It is a fact that these tools remain quite daunting for the majority of scientists working at the bench. The routine use of these methods is not simply a matter of education and training. Integration of these methods into accessible and robust end user software, without dilution of the science, must be a priority. We have reached a coincidence, where several technologies have the required level of maturity predictive computational chemistry methods, algorithms that manage the combinatorial explosion, high throughput crystallography and ADME measurements and the massive increase in computational horsepower from distributed computing. The author is confident that the synergy of these technologies will bring great benefit to the industry, with more efficient production of higher quality clinical candidates. The future is bright. The future is virtual!

LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites

We present a new shape-based method, LigandFit, for accurately docking ligands into protein active sites. The method employs a cavity detection algorithm for detecting invaginations in the protein as candidate active site regions. A shape comparison filter is combined with a Monte Carlo conformational search for generating ligand poses consistent with the active site shape. Candidate poses are minimized in the context of the active site using a grid-based method for evaluating protein-ligand interaction energies. Errors arising from grid interpolation are dramatically reduced using a new non-linear interpolation scheme. Results are presented for 19 diverse protein-ligand complexes. The method appears quite promising, reproducing the X-ray structure ligand pose within an RMS of 2A in 14 out of the 19 complexes. A high-throughput screening study applied to the thymidine kinase receptor is also presented in which LigandFit, when combined with LigScore, an internally developed scoring function, yields very good hit rates for a ligand pool seeded with known actives.

Rapid synthesis of triazine inhibitors of inosine monophosphate dehydrogenase

A series of novel triazine-based small molecule inhibitors (IV) of inosine monophosphate dehydrogenase was prepared. The synthesis and the structure-activity relationships (SAR) derived from in vitro studies are described.

Structure-based combinatorial library design: methodologies and applications

Rational design of small focused libraries that are biased toward specific therapeutic targets is currently at the forefront of combinatorial library design. Various structure-based design strategies can be implemented in focused library design when the 3D structure of the target is available through X-ray or NMR determination. This review discusses the major methods and programs specifically developed for the purpose of designing combinatorial libraries under the constraint of the binding site of a biological target, with emphasis on their advantages and disadvantages. Examples of the successful application of these methodologies are highlighted, demonstrating their performances within the practical drug discovery process.

Combinatorial informatics in the post-genomics ERA

The multitude of potential drug targets emerging from genome sequencing demands new approaches to drug discovery. A chemogenomics strategy, which involves the generation of small-molecule compounds that can be used both as tools to probe biological mechanisms and as leads for drug-property optimization, provides a highly parallel, industrialized solution. Key to the success of this strategy is an integrated suite of chemi-informatics applications that can allow the rapid and directed optimization of chemical compounds with drug-like properties using 'just-in-time' combinatorial chemical synthesis. An effective embodiment of this process requires new computational and data-mining tools that cover all aspects of library generation, compound selection and experimental design, and work effectively on a massive scale.

Challenges for biomedical informatics and pharmacogenomics

Pharmacogenomics requires the integration and analysis of genomic, molecular, cellular, and clinical data, and it thus offers a remarkable set of challenges to biomedical informatics. These include infrastructural challenges such as the creation of data models and databases for storing these data, the integration of these data with external databases, the extraction of information from natural language text, and the protection of databases with sensitive information. There are also scientific challenges in creating tools to support gene expression analysis, three-dimensional structural analysis, and comparative genomic analysis. In this review, we summarize the current uses of informatics within pharmacogenomics and show how the technical challenges that remain for biomedical informatics are typical of those that will be confronted in the postgenomic era.

High-throughput docking for lead generation

Recent improvements in flexible docking technology may lead to a bigger role for computational methods in lead discovery. Although fast and accurate computational prediction of binding affinities for an arbitrary molecule is still beyond the limits of current methods, the docking and screening procedures can select small sets of likely lead candidates from large libraries of either commercially or synthetically available compounds.

Design, docking, and evaluation of multiple libraries against multiple targets

We present a general approach to the design, docking, and virtual screening of multiple combinatorial libraries against a family of proteins. The method consists of three main stages: docking the scaffold, selecting the best substituents at each site of diversity, and comparing the resultant molecules within and between the libraries. The core "divide-and-conquer" algorithm for side-chain selection, developed from an earlier version (Sun et al., J Comp Aided Mol Design 1998;12:597-604), provides a way to explore large lists of substituents with linear rather than combinatorial time dependence. We have applied our method to three combinatorial libraries and three serine proteases: trypsin, chymotrypsin, and elastase. We show that the scaffold docking procedure, in conjunction with a novel vector-based orientation filter, reproduces crystallographic binding modes. In addition, the free-energy-based scoring procedure (Zou et al., J Am Chem Soc 1999;121:8033-8043) is able to reproduce experimental binding data for P1 mutants of macromolecular protease inhibitors. Finally, we show that our method discriminates between a peptide library and virtual libraries built on benzodiazepine and tetrahydroisoquinolinone scaffolds. Implications of the docking results for library design are explored.

Synergy between combinatorial chemistry and de novo design

Traditional de novo design algorithms are able to generate many thousands of ligand structures that meet the constraints of a protein structure, but these structures are often not synthetically tractable. In this article, we describe how concepts from structure-based de novo design can be used to explore the search space in library design. A key feature of the approach is the requirement that specific templates are included within the designed structures. Each template corresponds to the "central core" of a combinatorial library. The template is positioned within an acyclic chain whose length and bond orders are systematically varied, and the conformational space of each structure that results (core plus chain) is explored to determine whether it is able to link together two or more strongly interacting functional groups or pharmacophores located within a protein binding site. This fragment connection algorithm provides "generic" 3D molecules in the sense that the linking part (minus the template) is built from an all-carbon chain whose synthesis may not be easily achieved. Thus, in the second phase, 2D queries are derived from the molecular skeletons and used to identify possible reagents from a database. Each potential reagent is checked to ensure that it is compatible with the conformation of its parent 3D conformation and the constraints of the binding site. Combinations of these reagents according to the combinatorial library reaction scheme give product molecules that contain the desired core template and the key functional/pharmacophoric groups, and would be able to adopt a conformation compatible with the original molecular skeleton without any unfavorable intermolecular or intramolecular interactions. We discuss how this strategy compares with and relates to alternative approaches to both structure-based library design and de novo design.

Library design using BCUT chemistry-space descriptors and multiple four-point pharmacophore fingerprints: simultaneous optimization and structure-based diversity

New applications of fingerprints of multiple potential 4-point three-dimensional (3D) pharmacophores in combinatorial library design and virtual screening are presented. Preliminary results demonstrating the feasibility of a simulated annealing process for combinatorial reagent selection that concurrently optimizes product diversity in BCUT chemistry space and in terms of unique 4-point pharmacophores are discussed, and the advantage of using a customized chemistry-space derived for the library design is demonstrated. In addition, an extension to the multiple pharmacophore method for structure-based design that uses the shape of the target site as an additional constraint is presented. This development enables the docking process to be quantified in terms of the number and identities of the pharmacophoric hypotheses that can be matched by a compound or a library of compounds. The design of an example combinatorial library based on the Ugi condensation reaction and a serine protease active site is described.

Structure-based library design: molecular modelling merges with combinatorial chemistry

Recent advances in both computational and experimental techniques now allow a very fruitful interplay of computational and combinatorial chemistry in the structure-based design of combinatorial libraries.

Towards understanding the mechanisms of molecular recognition by computer simulations of ligand-protein interactions

The thermodynamic and kinetic aspects of molecular recognition for the methotrexate (MTX)-dihydrofolate reductase (DHFR) ligand-protein system are investigated by the binding energy landscape approach. The impact of 'hot' and 'cold' errors in ligand mutations on the thermodynamic stability of the native MTX-DHFR complex is analyzed, and relationships between the molecular recognition mechanism and the degree of ligand optimization are discussed. The nature and relative stability of intermediates and thermodynamic phases on the ligand-protein association pathway are studied, providing new insights into connections between protein folding and molecular recognition mechanisms, and cooperativity of ligand-protein binding. The results of kinetic docking simulations are rationalized based on the thermodynamic properties determined from equilibrium simulations and the shape of the underlying binding energy landscape. We show how evolutionary ligand selection for a receptor active site can produce well-optimized ligand-protein systems such as MTX-DHFR complex with the thermodynamically stable native structure and a direct transition mechanism of binding from unbound conformations to the unique native structure.

DREAM++: flexible docking program for virtual combinatorial libraries

We present a set of programs, DREAM+2 (Docking and Reaction programs using Efficient seArch Methods written in C++), for docking computationally generated ligands into macromolecular binding sites. DREAM++ is composed of three programs: ORIENT++, REACT++ and SEARCH++. The program ORIENT++ positions molecules in a binding site with the DOCK algorithm. Its output can be used as input to REACT++ and SEARCH+2. The program REACT++ performs user-specific chemical reactions on a docked molecule, so that reaction products can be evaluated for three dimensional complementarity with the macromolecular site. The program SEARCH++ performs an efficient conformation search on the reaction products using a hybrid backtrack and incremental construction algorithm. We have applied the programs to HIV protease-inhibitor complexes as test systems. We found that we can differentiate high-affinity ligands based on several measures: interaction energies, occupancy of protein subsites and the number of successfully docked conformations for each product. Encouraged by the results in the test case, we applied the programs to propose novel inhibitors of HIV protease. These inhibitors can be generated by organic reactions using commercially available reagents. They are alternatives to the inhibitors synthesized by Glaxo.