A validation study on the practical use of automated de novo design

Integrating Reaction Schemes, Reagent Databases, and Virtual Libraries into Fragment-Based Design by Reinforcement Learning

Lead optimization supported by artificial intelligence (AI)-based generative models has become increasingly important in drug design. Success factors are reagent availability, novelty, and the optimization of multiple properties. Directed fragment-replacement is particularly attractive, as it mimics medicinal chemistry tactics. Here, we present variations of fragment-based reinforcement learning using an actor-critic model. Novel features include freezing fragments and using reagents as the fragment source. Splitting molecules according to reaction schemes improves synthesizability, while tuning network output probabilities allows us to balance novelty versus diversity. Combining fragment-based optimization with virtual library encodings allows the exploration of large chemical spaces with synthesizable ideas. Collectively, these enhancements influence design toward high-quality molecules with favorable profiles. A validation study using 15 pharmaceutically relevant targets reveals that novel structures are obtained for most cases, which are identical or related to independent validation sets for each target. Hence, these modifications significantly increase the value of fragment-based reinforcement learning for drug design. The code is available on GitHub: https://github.com/Sanofi-Public/IDD-papers-fragrl.

Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods

The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into "drug-like" chemical space, such as target-activity machine learning models, respectively.

Molecular Design Method Using a Reversible Tree Representation of Chemical Compounds and Deep Reinforcement Learning

Automatic design of molecules with specific chemical and biochemical properties is an important process in material informatics and computational drug discovery. In this study, we designed a novel coarse-grained tree representation of molecules (Reversible Junction Tree; “RJT”) for the aforementioned purposes, which is reversely convertible to the original molecule without external information. By leveraging this representation, we further formulated the molecular design and optimization problem as a tree-structure construction using deep reinforcement learning (“RJT-RL”). In this method, all of the intermediate and final states of reinforcement learning are convertible to valid molecules, which could efficiently guide the optimization process in simple benchmark tasks. We further examined the multiobjective optimization and fine-tuning of the reinforcement learning models using RJT-RL, demonstrating the applicability of our method to more realistic tasks in drug discovery.

Artificial Intelligence in Compound Design

Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.

Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning

In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug design. Yet, these methods face the problem of data scarcity while designing drugs against a novel target. In this work, the potential of deep learning and molecular modeling approaches was leveraged to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets. Inhibitors of the homologues of the target protein were screened at the active site of the target protein to create an initial target-specific dataset. Transfer learning was used to learn the features of the target-specific dataset. A deep predictive model was utilized to predict the docking scores of newly designed molecules. Both these models were combined using reinforcement learning to design new chemical entities with an optimized docking score. The pipeline was validated by designing inhibitors against the human JAK2 protein, where none of the existing JAK2 inhibitors were used for training. The ability of the method to reproduce existing molecules from the validation dataset and design molecules with better binding energy demonstrates the potential of the proposed approach.

Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects of artificial intelligence based de novo design pertaining to its integration into real-life workflows. First, different chemical spaces were used as training sets for reinforcement learning (RL) in combination with different reward functions. With the trained neuronal networks different biologically active molecules could be regenerated. Excluding molecules with substructures such as five-membered rings from training spaces nevertheless produced results containing these moieties. Furthermore, different scoring functions in RL were investigated and produced different design ensembles. In summary, some of these design proposals are close in chemical space to the query, thus supporting lead optimization, while 3D-shape or QSAR (quantitative structure-activity relationship) models produced significantly different proposals by sampling a broader region of the chemical space, thus supporting lead generation. Therefore, RL provides a good framework to tailored design approaches for different discovery phases.

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.

Fragment-based design of kinase inhibitors: a practical guide

Fragment-based drug design has become an important strategy for drug design and development over the last decade. It has been used with particular success in the development of kinase inhibitors, which are one of the most widely explored classes of drug targets today. The application of fragment-based methods to discovering and optimizing kinase inhibitors can be a complicated and daunting task; however, a general process has emerged that has been highly fruitful. Here a practical outline of the fragment process used in kinase inhibitor design and development is laid out with specific examples. A guide to the overall process from initial discovery through fragment screening, including the difficulties in detection, to the computational methods available for use in optimization of the discovered fragments is reported.

Computational methods in drug discovery

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.

Automated design of ligands to polypharmacological profiles

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

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.

e-LEA3D: a computational-aided drug design web server

e-LEA3D web server integrates three complementary tools to perform computer-aided drug design based on molecular fragments. In drug discovery projects, there is a considerable interest in identifying novel and diverse molecular scaffolds to enhance chances of success. The de novo drug design tool is used to invent new ligands to optimize a user-specified scoring function. The composite scoring function includes both structure- and ligand-based evaluations. The de novo approach is an alternative to a blind virtual screening of large compound collections. A heuristic based on a genetic algorithm rapidly finds which fragments or combination of fragments fit a QSAR model or the binding site of a protein. While the approach is ideally suited for scaffold-hopping, this module also allows a scan for possible substituents to a user-specified scaffold. The second tool offers a traditional virtual screening and filtering of an uploaded library of compounds. The third module addresses the combinatorial library design that is based on a user-drawn scaffold and reactants coming, for example, from a chemical supplier. The e-LEA3D server is available at: http://bioinfo.ipmc.cnrs.fr/lea.html.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Hit identification and binding mode predictions by rigorous free energy simulations

The identification of lead molecules using computational modeling often relies on approximate, high-throughput approaches, of limited accuracy. We show here that, with a methodology we recently developed, it is possible to predict the relative binding free energies of structurally diverse ligands of the estrogen receptor-alpha using a rigorous statistical thermodynamics approach. Predictions obtained from the simulations with an explicit solvation model are in good qualitative agreement with experimental data, while simulations with implicit solvent models or rank ordering by empirical scoring functions yield predictions of lower quality. In addition, it is shown that free energy techniques can be used to select the most likely binding mode from a set of possible orientations generated by a docking program. It is suggested that the free energy techniques outlined in this study can be used to rank-order, by potency, structurally diverse compounds identified by virtual screening, de novo design or scaffold hopping programs.

Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization

We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.

De novo ligand design to partially flexible active sites: application of the ReFlex algorithm to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor

Reflex is a recent algorithm in the de novo ligand design software, SkelGen, that allows the flexibility of amino acid side chains in a protein to be taken into account during the drug-design process. In this paper the impact of flexibility on the solutions generated by the de novo design algorithm, when applied to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor (ER), is investigated. The results for each of the targets indicate that when allowing side-chain movement in the active site, solutions are generated that were not accessible from the multiple static protein conformations available for these targets. Furthermore, an analysis of structures generated in a flexible versus a static ER active site suggests that these additional solutions are not merely noise but contain many interesting chemotypes.

Tagged fragment method for evolutionary structure-based de novo lead generation and optimization

Here we describe a computer-assisted de novo drug design method, EAISFD, which combines the de novo design engine EA-Inventor with a scoring function featuring the molecular docking program Surflex-Dock. This method employs tagged fragments, which are preserved substructures in EA-Inventor used for base fragment matching in Surflex-Dock, for constructing ligand structures under specific binding motifs. In addition, a target score mechanism is adopted that allows EAISFD to deliver a diverse set of desired structures. This method can be used to design novel ligand scaffolds (lead generation) or to optimize attachments on a fixed scaffold (lead optimization). EAISFD has successfully suggested many known inhibitor scaffolds as well as a number of new scaffold types when applied to p38 MAP kinase.

A new class of histamine H3 receptor antagonists derived from ligand based design

Design and synthesis of highly potent and selective non-imidazole inverse agonists for the histamine H(3) receptor is described. The study validates a new pharmacophore model based on the merging of two previously described models. It also demonstrates that the removal of the basic center potentially interacting with ASP3.32 and common to both models leads to loss of activity, whereas the replacement of the second basic center by an acceptor retains the potency.

Chemical genomics: a challenge for de novo drug design

De novo design provides an in silico toolkit for the design of novel small molecular structures to a set of specified structural constraints. With the avalanche of bioinformatics data, de novo design is ideally suited for exploring molecules that could be useful for chemical genomics. The design process involves manipulation of the input, modification of structural constraints, and further processing of the de novo generated molecules using various modular toolkits. The development of a theoretical framework for each of these stages will provide novel practical solutions to the problem of creating compounds with maximal chemical diversity. This short review describes the fundamental problems encountered in the application of novel chemical design technologies to chemical genomics by means of a formal representation. This notation helps to outline and clarify ideas and hypotheses that can then be explored using mathematical algorithms. It is only by developing this rigorous foundation that in silico design can progress in a rational way.

FlexNovo: structure-based searching in large fragment spaces

We present a new molecular design program, FlexNovo, for structure-based searching within large fragment spaces following a sequential growth strategy. The fragment spaces consist of several thousands of chemical fragments and a corresponding set of rules that specify how the fragments can be connected. FlexNovo is based on the FlexX molecular docking software and makes use of its incremental construction algorithm and the underlying chemical models. Interaction energies are calculated by using standard scoring functions. Several placement geometry, physicochemical property (drug-likeness), and diversity filter criteria are directly integrated into the "build-up" process. FlexNovo has been used to design potential inhibitors for four targets of pharmaceutical interest (dihydrofolate reductase, cyclin-dependant kinase 2, cyclooxygenase-2, and the estrogen receptor). We have carried out calculations using different diversity parameters for each of these targets and generated solution sets containing up to 50 molecules. The compounds obtained show that FlexNovo is able to generate a diverse set of reasonable molecules with drug-like properties. The results, including an automated similarity analysis with the Feature Tree program, indicate that FlexNovo often reproduces structural motifs as well as the corresponding binding modes seen in known active structures.

Small-Molecule Inhibitors of the MDM2-p53 Protein−Protein Interaction Based on an Isoindolinone Scaffold

From a set of weakly potent lead compounds, using in silico screening and small library synthesis, a series of 2-alkyl-3-aryl-3-alkoxyisoindolinones has been identified as inhibitors of the MDM2-p53 interaction. Two of the most potent compounds, 2-benzyl-3-(4-chlorophenyl)-3-(3-hydroxypropoxy)-2,3-dihydroisoindol-1-one (76; IC(50) = 15.9 +/- 0.8 microM) and 3-(4-chlorophenyl)-3-(4-hydroxy-3,5-dimethoxybenzyloxy)-2-propyl-2,3-dihydroisoindol-1-one (79; IC(50) = 5.3 +/- 0.9 microM), induced p53-dependent gene transcription, in a dose-dependent manner, in the MDM2 amplified, SJSA human sarcoma cell line.

Drug Guru: a computer software program for drug design using medicinal chemistry rules

Drug Guru (drug generation using rules) is a new web-based computer software program for medicinal chemists that applies a set of transformations, that is, rules, to an input structure. The transformations correspond to medicinal chemistry design rules-of-thumb taken from the historical lore of drug discovery programs. The output of the program is a list of target analogs that can be evaluated for possible future synthesis. A discussion of the features of the program is followed by an example of the software applied to sildenafil (Viagra) in generating ideas for target analogs for phosphodiesterase inhibition. Comparison with other computer-assisted drug design software is given.

Exhaustive de novo design of low-molecular-weight fragments against the ATP-binding site of DNA-gyrase

We present a de novo design approach to generating small fragments in the DNA-gyrase ATP-binding site using the computational drug design platform SkelGen. We have generated an exhaustive number of structural possibilities, which were subsequently filtered for site complementarity and synthetic tractability. A number of known active fragments are found, but most of the species created are potentially novel and could be valuable for further elaboration and development into lead-like structures.

Structural biology and drug discovery

It has long been recognized that knowledge of the 3D structures of proteins has the potential to accelerate drug discovery, but recent developments in genome sequencing, robotics and bioinformatics have radically transformed the opportunities. Many new protein targets have been identified from genome analyses and studied by X-ray analysis or NMR spectroscopy. Structural biology has been instrumental in directing not only lead optimization and target identification, where it has well-established roles, but also lead discovery, now that high-throughput methods of structure determination can provide powerful approaches to screening.

Integrating molecular design resources within modern drug discovery research: the Roche experience

Various computational disciplines, such as cheminformatics, ADME modeling, virtual screening, chemogenomics search strategies and classic structure-based design, should be seen as one multifaceted discipline contributing to the early drug discovery process. Although significant resources enabling these activities have been established, their true integration into daily research should not be taken for granted. This article reviews value-adding activities from target assessment to lead optimization, and highlights the technical and process-related aspects that can be considered essential for performance and alignment within the research organization.

De novo ligand design to an ensemble of protein structures

We describe a combinatorial method for de novo ligand design to an ensemble of receptor structures. Receptor conformations, protonation states, and structural water molecules are considered consistently within the framework of de novo ligand design. The method relies on Monte Carlo optimization to search the space of ligand structures, conformations, and rigid-body movements as well as receptor models. The method is applied to an ensemble of HIV protease and human collagenase receptor models. Ligand structures generated de novo exhibit the correct hydrogen-bonding pattern in the core of the active site, with hydrophobic groups extending into the receptor S1 and S1' pocket space. Furthermore, it is shown that known ligands are recovered in the correct binding mode and in the native, most tightly binding receptor model.

Peptide, Peptidomimetic, and Small-molecule Antagonists of the p53-HDM2 Protein-Protein Interaction

Modulation of intracellular protein-protein interactions has been - and remains - a challenging goal for the discovery and development of small-molecule therapeutic agents. Progress in the pharmacological targeting and understanding at the molecular level of one such interaction that is relevant to cancer drug research, viz. that between the tumour suppressor protein p53 and its negative regulator HDM2, is reviewed here. The first X-ray crystal structure of a complex between a small peptide from the trans-activation domain of p53 and the N-terminal domain of HDM2 was reported almost 10 years ago. The nature of this interaction, which involves just three residue side chains in the p53 peptide ligand and a compact hydrophobic binding pocket in the HDM2 receptor, together with the attractive concept of reactivating the anti-proliferative functions of p53 in tumour cells, has spurned a great deal of effort aimed at finding drug-like antagonists of this interaction. A variety of approaches, including both structure-guided peptidomimetic and de novo design, as well as high through-put screening campaigns, have provided a wealth of leads that might be turned into actual drugs. There is still some way to go as far as optimisation and preclinical development of such leads is concerned, but it is clear already now that antagonists of the p53-HDM2 protein-protein interaction have a good chance of ultimately being successful in providing a new anti-cancer therapy modality, both in monotherapy and to potentiate the effectiveness of existing chemotherapies.

The effect of a tightly bound water molecule on scaffold diversity in the computer-aided de novo ligand design of CDK2 inhibitors

We have determined the effects that tightly bound water molecules have on the de novo design of cyclin-dependent kinase-2 (CDK2) ligands. In particular, we have analyzed the impact of a specific structural water molecule on the chemical diversity and binding mode of ligands generated through a de novo structure-based ligand generation method in the binding site of CDK2. The tightly bound water molecule modifies the size and shape of the binding site and we have found that it also imposed constraints on the observed binding modes of the generated ligands. This in turn had the indirect effect of reducing the chemical diversity of the underlying molecular scaffolds that were able to bind to the enzyme satisfactorily. [Figure: see text].

Generation and Selection of Novel Estrogen Receptor Ligands Using the De Novo Structure-Based Design Tool, SkelGen

A de novo design approach to generating novel estrogen receptor (ER) ligands is described. The SkelGen program was used to generate ligands in the active sites of seven crystal structures of ERalpha. Seventeen high-scoring, diverse structures were selected from the SkelGen output and synthesized without introducing any modifications to the structures. Five ligands, four of which are novel, showed < or = 25 microM affinity, with the best compound displaying an IC50 of 340 nM. SkelGen can, therefore, be a powerful tool for designing active molecules.

Receptor Flexibility in de Novo Ligand Design and Docking

One of the major problems in computational drug design is incorporation of the intrinsic flexibility of protein binding sites. This is particularly crucial in ligand binding events, when induced fit can lead to protein structure rearrangements. As a consequence of the huge conformational space available to protein structures, receptor flexibility is rarely considered in ligand design procedures. In this work, we present an algorithm for integrating protein binding-site flexibility into de novo ligand design and docking processes. The approach allows dynamic rearrangement of amino acid side chains during the docking and design simulations. The impact of protein conformational flexibility is investigated in the docking of highly active inhibitors in the binding sites of acetylcholinesterase and human collagenase (matrix metalloproteinase-1) and in the design of ligands in the S1' pocket of MMP-1. The results of corresponding simulations for both rigid and flexible binding sites are compared in order to gauge the influence of receptor flexibility in drug discovery protocols.

A robust clustering method for chemical structures

A clustering method based on finding the largest set of disconnected fragments that two chemical compounds have in common is shown to be able to group structures in a way that is ideally suited to medicinal chemistry programs. We describe how markedly improved results can be obtained by using a similarity metric that accounts not just for the size of the shared fragments but also on their relative arrangement in the two parent compounds. The use of a physiochemical atom typing scheme is also shown to provide significant contributions. Results from calculations using a test set consisting of actives from nine different important biological target proteins demonstrate the strengths of our clustering method and the advantages over other approaches that are widely used throughout the pharmaceutical industry.

Validation and use of the MM-PBSA approach for drug discovery

The MM-PBSA approach has become a popular method for calculating binding affinities of biomolecular complexes. Published application examples focus on small test sets and few proteins and, hence, are of limited relevance in assessing the general validity of this method. To further characterize MM-PBSA, we report on a more extensive study involving a large number of ligands and eight different proteins. Our results show that applying the MM-PBSA energy function to a single, relaxed complex structure is an adequate and sometimes more accurate approach than the standard free energy averaging over molecular dynamics snapshots. The use of MM-PBSA on a single structure is shown to be valuable (a) as a postdocking filter in further enriching virtual screening results, (b) as a helpful tool to prioritize de novo design solutions, and (c) for distinguishing between good and weak binders (DeltapIC(50) > or = 2-3), but rarely to reproduce smaller free energy differences.

Isoindolinone-based inhibitors of the MDM2-p53 protein-protein interaction

A series of 2-N-alkyl-3-aryl-3-alkoxyisoindolinones has been synthesised and evaluated as inhibitors of the MDM2-p53 interaction. The most potent compound, 3-(4-chlorophenyl)-3-(4-hydroxy-3,5-dimethoxybenzyloxy)-2-propyl-2,3-dihydroisoindol-1-one (NU8231), exhibited an IC50 of 5.3 +/- 0.9 microM in an ELISA assay, and induced p53-dependent gene transcription in a dose-dependent manner, in the SJSA human sarcoma cell line.

Including Tightly-Bound Water Molecules in de Novo Drug Design. Exemplification through the in Silico Generation of Poly(ADP-ribose)polymerase Ligands

Different strategies for the in silico generation of ligand molecules in the binding site of poly(ADP-ribose)polymerase (PARP) were studied in order to observe the effect of the targeting and displacement of tightly bound water molecules. Several molecular scaffolds were identified as having better interactions in the binding site when targeting one or two tightly bound water molecules in the NAD binding site. Energy calculations were conducted in order to assess the ligand-protein and ligand-water-protein interactions of different functional groups of the generated ligands. These calculations were used to evaluate the energetic consequences of the presence of tightly bound water molecules and to identify those that contribute favorably to the binding of ligands.

Combinatorial Ligand Design Targeted at Protein Families

We describe a method to create ligands specific for a given protein family. The method is applied to generate ligand candidates for the cyclin-dependent kinase (CDK) family. The CDK family of proteins is involved in regulating the cell cycle by alternately activating and deactivating the cell's progression through the cycle. CDKs are activated by association with cyclin and are inhibited by complexation with small molecules. X-ray crystal structures are available for three of the thirteen known CDK family members: CDK2, CDK5 and CDK 6. In this work, we use novel computational approaches to design ligand candidates that are potentially inhibitory across the three CDK family members as well as more specific molecules which can potentially inhibit one or any combination of two of the three CDK family members. We define a new scoring term, SpecScore, to quantify the potential inhibitory power of the generated structures. According to a search of the World Drug Alerts, the highest scoring SpecScore molecule that is specific for the three CDK family members shows very similar chemical characteristics and functional groups to numerous molecules known to deactivate several members of the CDK family.

Computer-aided design of small molecules for chemical genomics

De novo design provides an in silico toolkit for the design of novel molecular structures to a set of specified structural constraints, and is thus ideally suited for creating molecules for chemical genomics. The design process involves manipulation of the input, modification of structural constraints, and further processing of the de novo-generated molecules using various modular toolkits. The development of a theoretical framework for each of these stages will provide novel practical solutions to the problem of creating compounds with maximal chemical diversity. This chapter describes the fundamental problems encountered in the application of novel chemical design technologies to chemical genomics by means of a formal representation. Formal representations help to outline and clarify ideas and hypotheses that can then be explored using mathematical algorithms. It is only by developing this rigorous foundation, that in silico design can progress in a rational way.