PRO-LIGAND: an approach to de novo molecular design. 1. Application to the design of organic molecules

Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low

Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model-autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART-another deep learning generative model, but exploiting similar principles of navigation in a chemical space.

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space

Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.

BMaps: A Web Application for Fragment-Based Drug Design and Compound Binding Evaluation

Fragment-based drug design uses data about where, and how strongly, small chemical fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have been successfully using fragment data, derived from thermodynamically rigorous Monte Carlo fragment-protein binding simulations, in dozens of preclinical drug programs. However, this approach has not been available to the broader research community because of the cost and complexity of doing simulations and using design tools. We have developed a web application, called BMaps, to make fragment-based drug design widely available with greatly simplified user interfaces. BMaps provides access to a large repository (>550) of proteins with 100s of precomputed fragment maps, druggable hot spots, and high-quality water maps. Users can also employ their own structures or those from the Protein Data Bank and AlphaFold DB. Multigigabyte data sets are searched to find fragments in bondable orientations, ranked by a binding-free energy metric. The designers use this to select modifications that improve affinity and other properties. BMaps is unique in combining conventional tools such as docking and energy minimization with fragment-based design, in a very easy to use and automated web application. The service is available at https://www.boltzmannmaps.com.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

ReMODE: a deep learning-based web server for target-specific drug design

Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/ .

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.

RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design

Deep learning (DL)-based de novo molecular design has recently gained considerable traction. Many DL-based generative models have been successfully developed to design novel molecules, but most of them are ligand-centric and the role of the 3D geometries of target binding pockets in molecular generation has not been well-exploited. Here, we proposed a new 3D-based generative model called RELATION. In the RELATION model, the BiTL algorithm was specifically designed to extract and transfer the desired geometric features of the protein-ligand complexes to a latent space for generation. The pharmacophore conditioning and docking-based Bayesian sampling were applied to efficiently navigate the vast chemical space for the design of molecules with desired geometric properties and pharmacophore features. As a proof of concept, the RELATION model was used to design inhibitors for two targets, AKT1 and CDK2. The calculation results demonstrated that the RELATION model could efficiently generate novel molecules with favorable binding affinity and pharmacophore features.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Comprehensive assessment of deep generative architectures for de novo drug design

Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design.

Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking

Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (M^pro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.

Advances in de Novo Drug Design: From Conventional to Machine Learning Methods

. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.

AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4.

COVID-19 Coronavirus spike protein analysis for synthetic vaccines, a peptidomimetic antagonist, and therapeutic drugs, and analysis of a proposed achilles' heel conserved region to minimize probability of escape mutations and drug resistance

This paper continues a recent study of the spike protein sequence of the COVID-19 virus (SARS-CoV-2). It is also in part an introductory review to relevant computational techniques for tackling viral threats, using COVID-19 as an example. Q-UEL tools for facilitating access to knowledge and bioinformatics tools were again used for efficiency, but the focus in this paper is even more on the virus. Subsequence KRSFIEDLLFNKV of the S2′ spike glycoprotein proteolytic cleavage site continues to appear important. Here it is shown to be recognizable in the common cold coronaviruses, avian coronaviruses and possibly as traces in the nidoviruses of reptiles and fish. Its function or functions thus seem important to the coronaviruses. It might represent SARS-CoV-2 Achilles’ heel, less likely to acquire resistance by mutation, as has happened in some early SARS vaccine studies discussed in the previous paper. Preliminary conformational analysis of the receptor (ACE2) binding site of the spike protein is carried out suggesting that while it is somewhat conserved, it appears to be more variable than KRSFIEDLLFNKV. However compounds like emodin that inhibit SARS entry, apparently by binding ACE2, might also have functions at several different human protein binding sites. The enzyme 11β-hydroxysteroid dehydrogenase type 1 is again argued to be a convenient model pharmacophore perhaps representing an ensemble of targets, and it is noted that it occurs both in lung and alimentary tract. Perhaps it benefits the virus to block an inflammatory response by inhibiting the dehydrogenase, but a fairly complex web involves several possible targets.

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This paper “drills down” into the studies of the author's previous COVID-19 paper.

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Designing vaccine and drugs must seek to avoid escape mutations.

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Subsequence KRSFIEDLLFNKV seems recognizable across many coronaviruses.

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The ACE2 binding domain is a target, but shows variation.

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A steroid dehydrogenase is argued to remain an interesting model pharmacophore.

New Data Structure for Computational Molecular Design with Atomic or Fragment Resolution

A new molecular data structure and molecular structure operation algorithms are proposed for general purpose molecular design. The data structure allows for a variety of molecular operations for creating new molecules. Two types of molecular operations were developed, unimolecular and bimolecular operations. In unimolecular operations, a child molecule can be created from a parent via addition of a functional group, deletion of a fragment, mutation of an atom, etc. In bimolecular operations, children molecules are generated from two parent molecules through combination or crossover (hybridization). These molecular operations are essential for the creation and modification of molecules for the purpose of molecular design. The data structure is capable of representing linear, branched, multifunctional, and multivalent compounds. Algorithms are developed for deriving the molecular data structure of a molecule from its atomic coordinates and vice versa. We show that this new molecular data structure and the developed algorithms, referred to as Molecular Assembling and Representation Suite, allow one to generate a comprehensive library of new molecules via performing every possible molecular structure modification.

Approaching Target Selectivity by De Novo Drug Design

Introduction: The development of drug candidates with a defined selectivity profile and a unique molecular structure is of fundamental interest for drug discovery. In contrast to the costly screening of large substance libraries, the targeted de novo design of a drug by using structural information of either the biological target and/or structure-activity relationship data of active modulators offers an efficient and intellectually appealing alternative. Areas covered: This review provides an overview on the different techniques of de novo drug design (ligand-based drug design, structure-based drug design, and fragment-based drug design) and highlights successful examples of this targeted approach toward selective modulators of therapeutically relevant targets. Expert opinion: De novo drug design has established itself as a very efficient method for the development of potent and selective modulators for a variety of different biological target classes. The ever-growing wealth of structural data on therapeutic targets will certainly further enhance the importance of de novo design for the drug discovery process in the future. However, a consistent use of the terminology of de novo drug design in the scientific literature should be sought.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Active Search for Computer-aided Drug Design

We consider lead discovery as active search in a space of labelled graphs. In particular, we extend our recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an αv integrin, the target protein that belongs to a group of Arg-Gly-Asp integrin receptors. This group of integrin receptors is thought to play a key role in idiopathic pulmonary fibrosis, a chronic lung disease of significant pharmaceutical interest. As an in silico proxy of the binding affinity, we use a molecular docking score to an experimentally determined αvβ6 protein structure. The search is driven by a probabilistic surrogate of the activity of all molecules from that space. As the process evolves and the algorithm observes the activity scores of the previously designed molecules, the hypothesis of the activity is refined. The algorithm is guaranteed to converge in probability to the best hypothesis from an a priori specified hypothesis space. In our empirical evaluations, the approach achieves a large structural variety of designed molecular structures for which the docking score is better than the desired threshold. Some novel molecules, suggested to be active by the surrogate model, provoke a significant interest from the perspective of medicinal chemistry and warrant prioritization for synthesis. Moreover, the approach discovered 19 out of the 24 active compounds which are known to be active from previous biological assays.

De Novo Design of Ligands Using Computational Methods

De novo design technique is complementary to high-throughput virtual screening and is believed to contribute in pharmaceutical development of novel drugs with desired properties at a very low cost and time-efficient manner. In this chapter, we outline the basic de novo design concepts based on computational methods with an example.

Fragment-Based Ligand Designing

Fragment-based drug design strategies have been used in drug discovery since it was first demonstrated using experimental structural biology techniques such as nuclear magnetic resonance (NMR) and X-ray crystallography. The underlying idea is that existing or new chemical entities with known desirable properties may serve both as tool compounds and as starting points for hit-to-lead expansion. Despite the recent advancements, there remain challenges to overcome, such as assembly of the synthetically feasible structures, development of scoring functions to correlate structure and their activities, and fine tuning of the promising molecules. This chapter first covers the theoretical background needed to understand the concepts and the challenges related to the field of study, followed by the description of important protocols and related software. Case studies are presented to demonstrate practical applications.

Fragment-Based De Novo Design of Cyclin-Dependent Kinase 2 Inhibitors

Cyclin-dependent kinases (CDKs) are core components of the cell cycle machinery that govern the transition between phases during cell cycle progression. Abnormalities in CDKs activity and regulation are common features of cancer, making CDK family members attractive targets for the development of anticancer drugs. One of the main bottlenecks hampering the development of drugs for kinase is the difficulty to attain selectivity. A huge variety of small molecules have been reported as CDK inhibitors, as potential anticancer agents, but none of these has been approved for commercial use. Computer-based molecular design supports drug discovery by suggesting novel new chemotypes and compound modifications for lead candidate optimization. One of the methods known as de novo ligand design technique has emerged as a complementary approach to high-throughput screening. Several automated de novo software programs have been written, which automatically design novel structures to perfectly fit in known binding site. The de novo design supports drug discovery assignments by generating novel pharmaceutically active agents with desired properties in a cost as well as time efficient approach. This chapter describes procedure and an overview of computer-based molecular de novo design methods on a conceptual level with successful examples of CDKs inhibitors.

Computational Methods Applied to Rational Drug Design

Due to the synergic relationship between medical chemistry, bioinformatics and molecular simulation, the development of new accurate computational tools for small molecules drug design has been rising over the last years. The main result is the increased number of publications where computational techniques such as molecular docking, de novo design as well as virtual screening have been used to estimate the binding mode, site and energy of novel small molecules. In this work I review some tools, which enable the study of biological systems at the atomistic level, providing relevant information and thereby, enhancing the process of rational drug design.

LiGen: a high performance workflow for chemistry driven de novo design

Tools for molecular de novo design are actively sought incorporating sets of chemical rules for fast and efficient identification of structurally new chemotypes endowed with a desired set of biological properties. In this paper, we present LiGen, a suite of programs which can be used sequentially or as stand-alone tools for specific purposes. In its standard application, LiGen modules are used to define input constraints, either structure-based, through active site identification, or ligand-based, through pharmacophore definition, to docking and to de novo generation. Alternatively, individual modules can be combined in a user-defined manner to generate project-centric workflows. Specific features of LiGen are the use of a pharmacophore-based docking procedure which allows flexible docking without conformer enumeration and accurate and flexible reactant mapping coupled with reactant tagging through substructure searching. The full description of LiGen functionalities is presented.

AutoGrow 3.0: an improved algorithm for chemically tractable, semi-automated protein inhibitor design

We here present an improved version of AutoGrow (version 3.0), an evolutionary algorithm that works in conjunction with existing open-source software to automatically optimize candidate ligands for predicted binding affinity and other druglike properties. Though no substitute for the medicinal chemist, AutoGrow 3.0, unlike its predecessors, attempts to introduce some chemical intuition into the automated optimization process. AutoGrow 3.0 uses the rules of click chemistry to guide optimization, greatly enhancing synthesizability. Additionally, the program discards any growing ligand whose physical and chemical properties are not druglike. By carefully crafting chemically feasible druglike molecules, we hope that AutoGrow 3.0 will help supplement the chemist's efforts. To demonstrate the utility of the program, we use AutoGrow 3.0 to generate predicted inhibitors of three important drug targets: Trypanosoma brucei RNA editing ligase 1, peroxisome proliferator-activated receptor γ, and dihydrofolate reductase. In all cases, AutoGrow generates druglike molecules with high predicted binding affinities. AutoGrow 3.0 is available free of charge (http://autogrow.ucsd.edu) under the terms of the GNU General Public License and has been tested on Linux and Mac OS X.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

EvoMD: an algorithm for evolutionary molecular design

Traditionally, Computer-Aided Molecular Design (CAMD) uses heuristic search and mathematical programming to tackle the molecular design problem. But these techniques do not handle large and nonlinear search space very well. To overcome these drawbacks, graph-based evolutionary algorithms (EAs) have been proposed to evolve molecular design by mimicking chemical reactions on the exchange of chemical bonds and components between molecules. For these EAs to perform their tasks, known molecular components, which can serve as building blocks for the molecules to be designed, and known chemical rules, which govern chemical combination between different components, have to be introduced before the evolutionary process can take place. To automate molecular design without these constraints, this paper proposes an EA called Evolutionary Algorithm for Molecular Design (EvoMD). EvoMD encodes molecular designs in graphs. It uses a novel crossover operator which does not require known chemistry rules known in advanced and it uses a set of novel mutation operators. EvoMD uses atomics-based and fragment-based approaches to handle different size of molecule, and the value of the fitness function it uses is made to depend on the property descriptors of the design encoded in a molecular graph. It has been tested with different data sets and has been shown to be very promising.

Prediction of Positions of Active Compounds Makes It Possible To Increase Activity in Fragment-Based Drug Development

We have developed a computational method that predicts the positions of active compounds, making it possible to increase activity as a fragment evolution strategy. We refer to the positions of these compounds as the active position. When an active fragment compound is found, the following lead generation process is performed, primarily to increase activity. In the current method, to predict the location of the active position, hydrogen atoms are replaced by small side chains, generating virtual compounds. These virtual compounds are docked to a target protein, and the docking scores (affinities) are examined. The hydrogen atom that gives the virtual compound with good affinity should correspond to the active position and it should be replaced to generate a lead compound. This method was found to work well, with the prediction of the active position being 2 times more efficient than random synthesis. In the current study, 15 examples of lead generation were examined. The probability of finding active positions among all hydrogen atoms was 26%, and the current method accurately predicted 60% of the active positions.

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.

Fragment-based drug design

Fragment-based drug design (FBDD), which is comprised of both fragment screening and the use of fragment hits to design leads, began more than 15 years ago and has been steadily gaining in popularity and utility. Its origin lies on the fact that the coverage of chemical space and the binding efficiency of hits are directly related to the size of the compounds screened. Nevertheless, FBDD still faces challenges, among them developing fragment screening libraries that ensure optimal coverage of chemical space, physical properties and chemical tractability. Fragment screening also requires sensitive assays, often biophysical in nature, to detect weak binders. In this chapter we will introduce the technologies used to address these challenges and outline the experimental advantages that make FBDD one of the most popular new hit-to-lead process.

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 drug design using multiobjective evolutionary graphs

Drug discovery and development is a complex, lengthy process, and failure of a candidate molecule can occur as a result of a combination of reasons, such as poor pharmacokinetics, lack of efficacy, or toxicity. Successful drug candidates necessarily represent a compromise between the numerous, sometimes competing objectives so that the benefits to patients outweigh potential drawbacks and risks. De novo drug design involves searching an immense space of feasible, druglike molecules to select those with the highest chances of becoming drugs using computational technology. Traditionally, de novo design has focused on designing molecules satisfying a single objective, such as similarity to a known ligand or an interaction score, and ignored the presence of the multiple objectives required for druglike behavior. Recently, methods have appeared in the literature that attempt to design molecules satisfying multiple predefined objectives and thereby produce candidate solutions with a higher chance of serving as viable drug leads. This paper describes the Multiobjective Evolutionary Graph Algorithm (MEGA), a new multiobjective optimization de novo design algorithmic framework that can be used to design structurally diverse molecules satisfying one or more objectives. The algorithm combines evolutionary techniques with graph-theory to directly manipulate graphs and perform an efficient global search for promising solutions. In the Experimental Section we present results from the application of MEGA for designing molecules that selectively bind to a known pharmaceutical target using the ChillScore interaction score family. The primary constraints applied to the design are based on the identified structure of the protein target and a known ligand currently marketed as a drug. A detailed explanation of the key elements of the specific implementation of the algorithm is given, including the methods for obtaining molecular building blocks, evolving the chemical graphs, and scoring the designed molecules. Our findings demonstrate that MEGA can produce structurally diverse candidate molecules representing a wide range of compromises of the supplied constraints and thus can be used as an "idea generator" to support expert chemists assigned with the task of molecular design.

AutoGrow: a novel algorithm for protein inhibitor design

Due in part to the increasing availability of crystallographic protein structures as well as rapid improvements in computing power, the past few decades have seen an explosion in the field of computer-based rational drug design. Several algorithms have been developed to identify or generate potential ligands in silico by optimizing the ligand-receptor hydrogen bond, electrostatic, and hydrophobic interactions. We here present AutoGrow, a novel computer-aided drug design algorithm that combines the strengths of both fragment-based growing and docking algorithms. To validate AutoGrow, we recreate three crystallographically resolved ligands from their constituent fragments.

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.

The concept of template-based de novo design from drug-derived molecular fragments and its application to TAR RNA

Principles of fragment-based molecular design are presented and discussed in the context of de novo drug design. The underlying idea is to dissect known drug molecules in fragments by straightforward pseudo-retro-synthesis. The resulting building blocks are then used for automated assembly of new molecules. A particular question has been whether this approach is actually able to perform scaffold-hopping. A prospective case study illustrates the usefulness of fragment-based de novo design for finding new scaffolds. We were able to identify a novel ligand disrupting the interaction between the Tat peptide and TAR RNA, which is part of the human immunodeficiency virus (HIV-1) mRNA. Using a single template structure (acetylpromazine) as reference molecule and a topological pharmacophore descriptor (CATS), new chemotypes were automatically generated by our de novo design software Flux. Flux features an evolutionary algorithm for fragment-based compound assembly and optimization. Pharmacophore superimposition and docking into the target RNA suggest perfect matching between the template molecule and the designed compound. Chemical synthesis was straightforward, and bioactivity of the designed molecule was confirmed in a FRET assay. This study demonstrates the practicability of de novo design to generating RNA ligands containing novel molecular scaffolds.

Pocket v.2: further developments on receptor-based pharmacophore modeling

A deriving pharmacophore model from the three-dimensional structure of a target protein provides helpful information for analyzing protein-ligand interactions and further improvement of ligand binding affinity. A standalone program, Pocket v.2, has been developed based on the original Pocket module in the de novo drug design program LigBuilder. Pocket v.2 is able to derive a pharmacophore model directly from a given protein-ligand complex structure without human intervention. Key features in the pharmacophore model are automatically reduced to a reasonable number. Pocket v.2 has been applied to several case studies, including cyclin dependent kinase 2, HIV-1 protease, estrogen receptor, and 17beta-hydroxysteroid dehydrogenase. It well reproduced previously published pharmacophore models in all of these cases. One notable feature of Pocket v.2 is that it can tolerate minor conformational changes on the protein side upon binding of different ligands to give a consistent pharmacophore model. For different proteins accommodating the same ligand, Pocket v.2 gives similar pharmacophore models, which opens the possibility to classify proteins with their binding features.