Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with Forecaster, a novel platform for drug discovery

As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.     

Combinatorial chemistry: a tool for the discovery of new catalysts

The discovery of new reactions and catalysts has always presented an intriguing challenge to scientists. With the rise of combinatorial chemistry, a new method has emerged that holds considerable promise to facilitate the task since it allows for the simultaneous generation and testing of a large number of compounds. The crucial difficulty lies in establishing general technologies for rapid and reliable screening of libraries to determine the catalytic activity of their members. Several recent publications have addressed this question by using infrared thermography, colorimetric assays and fluorescence spectroscopy. These techniques have not only been applied successfully to the high-throughput screening of parallel compound arrays but also to the screening of one-bead-one-compound libraries. This demonstrates that combinatorial chemistry possesses indeed the potential to establish itself as a powerful tool for the discovery of new catalysts. This review describes the methodologies used so far for the detection of catalytic events and will place particular emphasis on the on-bead screening of one-bead-one-compound libraries.     

Library fingerprints: a novel approach to the screening of virtual libraries

We propose a novel method to prioritize libraries for combinatorial synthesis and high-throughput screening that assesses the viability of a particular library on the basis of the aggregate physical-chemical properties of the compounds using a na?ve Bayesian classifier. This approach prioritizes collections of related compounds according to the aggregate values of their physical-chemical parameters in contrast to single-compound screening. The method is also shown to be useful in screening existing noncombinatorial libraries when the compounds in these libraries have been previously clustered according to their molecular graphs. We show that the method used here is comparable or superior to the single-compound virtual screening of combinatorial libraries and noncombinatorial libraries and is superior to the pairwise Tanimoto similarity searching of a collection of combinatorial libraries.     

Receptor-assisted combinatorial chemistry: thermodynamics and kinetics in drug discovery

Current drug discovery using combinatorial chemistry involves synthesis followed by screening, but emerging methods involve receptor-assistance to combine these steps. Adding stoichiometric amounts of receptor during library synthesis alters the kinetics or thermodynamics of the synthesis in a way that identifies the best-binding library members. Three main methods have emerged thus far in receptor-assisted combinatorial chemistry: dynamic combinatorial libraries, receptor-accelerated synthesis, and a new method, pseudo-dynamic libraries. Pseudo-dynamic libraries apply both thermodynamics and kinetics to amplify library members to easily observable levels, and attain selectivity heretofore unseen in receptor-assisted systems.     

Applications of combinatorial chemistry in the agrosciences

During the past ten years combinatorial chemistry developed from a powerful synthetic methodology, providing large libraries of usually simple new chemical entities, to a comprehensive strategy presently covering a multitude of technologies across the whole workflow from hit generation to lead optimization. Thus combinatorial chemistry had a major impact not only on the pharmaceutical research but also with some delay on the agrochemical research. The agrochemical discovery environment is different from that of the pharmaceutical research in that it relies mainly on whole organism screenings. This review summarizes some recent applications of combinatorial chemistry in the agrosciences, covering all the three major fields of research: fungicides, herbicides, and insecticides. The article focuses on libraries with published biological activities and thus highlights some characteristic features of successful agrochemical libraries, which may be fundamentally different from pharmaceutical libraries.     

Computer-aided drug design at Boehringer Ingelheim

Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.     

Modern drug discovery technologies: opportunities and challenges in lead discovery

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.     

Library Design Guided by In-house Developed Computer Tools

In recent years, combinatorial library synthesis for drug discovery begins to migrate from library synthesis solely dictated by chemistry availability to design and synthesis of libraries with more drug-like properties. Lipinski's rule of five has been used to evaluate drug-like properties of individual compound; recently LibProTM, a new computation program has been developed at Pharmacopeia to evaluate durg-like properties of libraries. By using LibPrpTM, chemists at Pharmacopeia are able to obtain information of molecular weight and ClogP distribution of a library, and percentage of library members that violate Lipinski's rule after input structures of synthons for each combinatorial step. Currently, a "virtual library design” approach that is to calculate properties of a library at conceptual phase of the library design has been used to predetermine the value of the library. Also a new computer program used to predict "Absorption” of compounds will also be discussed.     

Machine Learning Methods in Drug Discovery

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.     

Virtual Screening and Structure Generation Applied to Drug Design

The methods of computer-aided drug design can be divided into two categories according to whether or not the structures of receptors are known1, corresponding to two principal strategies: (1) searching the bio-active ligands against virtual combinatorial libraries and calculating the affinity energy between ligand and receptor by docking ; (2) QSAR and 3D-structure data-mining. 3D-QSAR method is now applied widely to drug discovery, but this method is generally limited to refine the structu…     

Chip-based high-throughput screening of herbal medicines

At present, high-throughput screening (HTS) programs in drug discovery rely mainly on compound libraries from combinational chemistry. Similarly, natural flora has been used as a prominent origin for new and potent herbal drugs. Herbal medicines have been used worldwide for thousands of years to cure many diseases. As such, herbal secondary metabolites show a remarkable structural diversity that supplements chemically synthesized compound analogs in drug discovery screening. Unfortunately, there is often a considerable deterioration in the quality of herbal drugs in such screening programs as there are time-consuming manual processes involved in the isolation of active ingredients from the highly complex mixtures of herbal plant products. The quality and quantity of herbal samples are critical for the success of HTS programs. In the recent past, there have been substantial improvements in HTS due to the miniaturization and integration of microchip (e.g., Herbochip(?), DNA chip, protein chip, cell chip, etc.)-based technologies so as to design herbal drugs that compete with synthetic drug analogs. Here we will review various technologies used for HTS of herbal medicines. Finally, we will summarize our efforts to develop a novel chip-based HTS assay to explore the antioxidant and radioprotective properties of herbal plants.     

Combinatorial synthesis in micro reactors

This article reviews the current and future applications of micro reactors in the field of combinatorial chemistry and drug discovery. Liquid phase reactions have been used to illustrate the advantages of performing chemical reactions in micro reactors which illustrate that reactions can be performed very rapidly in high yield to enable the preparation of combinatorial libraries of structurally related compounds.     

Whole cell strategies based on lux genes for high throughput applications toward new antimicrobials

The discovery/development of novel drug candidates has witnessed dramatic changes over the last two decades. Old methods to identify lead compounds are not suitable to screen wide libraries generated by combinatorial chemistry techniques. High throughput screening (HTS) has become irreplaceable and hundreds of different approaches have been described. Assays based on purified components are flanked by whole cell-based assays, in which reporter genes are used to monitor, directly or indirectly, the influence of a chemical over the metabolism of living cells. The most convenient and widely used reporters for real-time measurements are luciferases, light emitting enzymes from evolutionarily distant organisms. Autofluorescent proteins have been also extensively employed, but proved to be more suitable for end-point measurements, in situ applications - such as the localization of fusion proteins in specific subcellular compartments - or environmental studies on microbial populations. The trend toward miniaturization and the technical advances in detection and liquid handling systems will allow to reach an ultra high throughput screening (uHTS), with 100,000 of compounds routinely screened each day. Here we show how similar approaches may be applied also to the search for new and potent antimicrobial agents.     

Past and future perspectives of synthetic peptide libraries

Combinatorial preparation and HTS of arrays of compounds have increased the speed of drug discovery. A strong impulse in this field has come by the introduction of the solid phase synthesis method that, through automation and miniaturization, has paved the way to the preparation of large collections of compounds in compact and trackable formats. Due to the well established synthetic procedures, peptides have been largely used to develop the basic concepts of combinatorial chemistry and peptide libraries are still successfully employed in screening programs. However, peptides generally do not fulfil the requirements of low conformational flexibility, stability and bioavailability needed for good drug candidates and peptide leads with high potency and selectivity are often made "druggable" by conversion to more stable structures with improved pharmacological profiles. Such an approach makes the screening of peptide libraries still a valuable tool for drug discovery. We propose here a panoramic review of the most common methods for the preparation and screening of peptide libraries and the most interesting findings of the last decade. We also report on a new approach we follow in our laboratory that is based on the use of "simplified" libraries composed by a minimum number of non-redundant amino acids for the assembly of short peptides. The choice of amino acids is dictated by diversity in lipophilicity, MW, charge and polarity. Newly identified active sequences are then modified by preparing new variants containing analogous amino acids, so that the chemical space occupied by the excluded residues can be explored. This approach offers the advantage of simplifying the synthesis and deconvolution of libraries and provides new active compounds with a molecular size similar to that of small molecules, to which they can be easily converted.