Can we really do computer-aided drug design?

In this article, we discuss what we mean by ‘design’ and contrast this with the application of computational methods in drug discovery. We suggest that the predictivity of the computational models currently applied in drug discovery is not yet sufficient to permit a true design paradigm, as demonstrated by the large number of compounds that must currently be synthesised and tested to identify a successful drug. However, despite the uncertainties in predictions, computational methods have enormous potential value in narrowing the range of compounds to consider, by eliminating those that have negligible chance of being a successful drug, while focussing efforts on chemistries with the best likelihood of success. Applied appropriately, computational approaches can support decision-makers in achieving multi-parameter optimisation to guide the selection and design of compounds with the best chance of achieving an appropriate balance of properties for a drug discovery project’s objectives. Finally, we consider some approaches that may contribute over the next 25 years to improve the accuracy and transferability of computational models in drug discovery and move towards a genuine design process.     

Bigger data,collaborative tools and the future of predictive drug discovery

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.     

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.     

Engaging undergraduate students in computational chemistry research: A tutorial for new assistant professors

In this article, we provide advice and insights, based on our own experiences, for computational chemists who are beginning new tenure-track positions at primarily undergraduate institutions. Each of us followed different routes to obtain our tenure-track positions, but we all experienced similar challenges when getting started in our new position. In this article, we discuss our approaches to seven areas that we all found important for engaging undergraduate students in our computational chemistry research, including setting up computational resources, recruiting research students, training research students, designing student projects, managing the lab, mentoring students, and student conference participation.     

Computer-aided drug discovery research at a global contract research organization

Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.     

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.     

The evolution of drug design at Merck Research Laboratories

On October 5, 1981, Fortune magazine published a cover article entitled the “Next Industrial Revolution: Designing Drugs by Computer at Merck”. With a 40+ year investment, we have been in the drug design business longer than most. During its history, the Merck drug design group has had several names, but it has always been in the “design” business, with the ultimate goal to provide an actionable hypothesis that could be tested experimentally. Often the result was a small molecule but it could just as easily be a peptide, biologic, predictive model, reaction, process, etc. To this end, the concept of design is now front and center in all aspects of discovery, safety assessment and early clinical development. At present, the Merck design group includes computational chemistry, protein structure determination, and cheminformatics. By bringing these groups together under one umbrella, we were able to align activities and capabilities across multiple research sites and departments. This alignment from 2010 to 2016 resulted in an 80% expansion in the size of the department, reflecting the increase in impact due to a significant emphasis across the organization to “design first” along the entire drug discovery path from lead identification (LID) to first in human (FIH) dosing. One of the major advantages of this alignment has been the ability to access all of the data and create an adaptive approach to the overall LID to FIH pathway for any modality, significantly increasing the quality of candidates and their probability of success. In this perspective, we will discuss how we crafted a new strategy, defined the appropriate phenotype for group members, developed the right skillsets, and identified metrics for success in order to drive continuous improvement. We will not focus on the tactical implementation, only giving specific examples as appropriate.     

Adapting the DeepSARM approach for dual-target ligand design

The structure–activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.

     

Design,Synthesis, and Evaluation of Bioactive Small Molecules

Collaborative research projects between chemists, biologists, and medical scientists have inevitably produced many useful drugs, biosensors, and medical instrumentation. Organic chemistry lies at the heart of drug discovery and development. The current range of organic synthetic methodologies allows for the construction of unlimited libraries of small organic molecules for drug screening. In translational research projects, we have focused on the discovery of lead compounds for three major diseases: Alzheimer's disease (AD), breast cancer, and viral infections. In the AD project, we have taken a rational‐design approach and synthesized a new class of tricyclic pyrone (TP) compounds that preserve memory and motor functions in amyloid precursor protein (APP)/presenilin‐1 (PS1) mice. TPs could protect neuronal death through several possible mechanisms, including their ability to inhibit the formation of both intraneuronal and extracellular amyloid β (Aβ) aggregates, to increase cholesterol efflux, to restore axonal trafficking, and to enhance long‐term potentiation (LTP) and restored LTP following treatment with Aβ oligomers. We have also synthesized a new class of gap‐junction enhancers, based on substituted quinolines, that possess potent inhibitory activities against breast‐cancer cells in vitro and in vivo. Although various antiviral drugs are available, the emergence of viral resistance to existing antiviral drugs and various understudied viral infections, such as norovirus and rotavirus, emphasizes the demand for the development of new antiviral agents against such infections and others. Our laboratories have undertaken these projects for the discovery of new antiviral inhibitors. The discussion of these aforementioned projects may shed light on the future development of drug candidates in the fields of AD, cancer, and viral infections.     

Investigation of innovative synthesis of biologically active compounds on the basis of newly developed reactions

Synthesis of biologically active compounds, including natural products and pharmaceutical agents, is an important and interesting research area since the large structural diversity and complexity of bioactive compounds make them an important source of leads and scaffolds in drug discovery and development. Many structurally and also biologically interesting compounds, including marine natural products, have been isolated from nature and have also been prepared on the basis of a computational design for the purpose of developing medicinal chemistry. In order to obtain a wide variety of derivatives of biologically active compounds from the viewpoint of medicinal chemistry, it is essential to establish efficient synthetic procedures for desired targets. Newly developed reactions should also be used for efficient synthesis of desired compounds. Thus, recent progress in the synthesis of biologically active compounds by focusing on the development of new reactions is summarized in this review article.     

Solid-state NMR, crystallographic, and computational investigation of bisphosphonates and farnesyl diphosphate synthase-bisphosphonate complexes

Bisphosphonates are a class of molecules in widespread use in treating bone resorption diseases and are also of interest as immunomodulators and anti-infectives. They function by inhibiting the enzyme farnesyl diphosphate synthase (FPPS), but the details of how these molecules bind are not fully understood. Here, we report the results of a solid-state (13)C, (15)N, and (31)P magic-angle sample spinning (MAS) NMR and quantum chemical investigation of several bisphosphonates, both as pure compounds and when bound to FPPS, to provide information about side-chain and phosphonate backbone protonation states when bound to the enzyme. We then used computational docking methods (with the charges assigned by NMR) to predict how several bisphosphonates bind to FPPS. Finally, we used X-ray crystallography to determine the structures of two potent bisphosphonate inhibitors, finding good agreement with the computational results, opening up the possibility of using the combination of NMR, quantum chemistry and molecular docking to facilitate the design of other, novel prenytransferase inhibitors.     

Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling

Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by focusing on an exemplary kinase of interest. Covalent inhibitors experience a renaissance in drug discovery, especially for targeting protein kinases. However, computational design of this class of inhibitors has thus far only been little investigated. To this end, we have devised a computational approach combining fragment-based design and deep generative modeling augmented by three-dimensional pharmacophore screening. This approach is thought to be particularly relevant for medicinal chemistry applications because it combines knowledge-based elements with deep learning and is chemically intuitive. As an exemplary application, we report for Bruton’s tyrosine kinase (BTK), a major drug target for the treatment of inflammatory diseases and leukemia, the generation of novel candidate inhibitors with a specific chemically reactive group for covalent modification, requiring only little target-specific compound information to guide the design efforts. Newly generated compounds include known inhibitors and characteristic substructures and many novel candidates, thus lending credence to the computational approach, which is readily applicable to other targets.     

Computation of the physio-chemical properties and data mining of large molecular collections

Very large data sets of molecules screened against a broad range of targets have become available due to the advent of combinatorial chemistry. This information has led to the realization that ADME (absorption, distribution, metabolism, and excretion) and toxicity issues are important to consider prior to library synthesis. Furthermore, these large data sets provide a unique and important source of information regarding what types of molecular shapes may interact with specific receptor or target classes. Thus, the requirement for rapid and accurate data mining tools became paramount. To address these issues Pharmacopeia, Inc. formed a computational research group, The Center for Informatics and Drug Discovery (CIDD).* In this review we cover the work done by this group to address both in silico ADME modeling and data mining issues faced by Pharmacopeia because of the availability of a large and diverse collection (over 6 million discrete compounds) of drug-like molecules. In particular, in the data mining arena we discuss rapid docking tools and how we employ them, and we describe a novel data mining tool based on a ID representation of a molecule followed by a molecular sequence alignment step. For the ADME area we discuss the development and application of absorption, blood-brain barrier (BBB) and solubility models. Finally, we summarize the impact the tools and approaches might have on the drug discovery process.     

Proteomimetic libraries: design, synthesis, and evaluation of p53-MDM2 interaction inhibitors

The p53-MDM2 interaction regulates p53-mediated cellular responses to DNA damage, and MDM2 is overexpressed in 7% of all cancers. Structure-based computational design was applied to this system to design libraries centered on a scaffold that projects side chain functionalities with distance and angular relationships equivalent to those seen in the MDM2 interacting motif of p53. A library of 173 such compounds was synthesized using solution phase parallel chemistry. The in vitro competitive ability of the compounds to block p53 peptide binding to MDM2 was determined using a fluorescence polarization competition assay. The most active compound bound with K(d) = 12 microM, and its binding was characterized by (15)N-(1)H HSQC NMR.     

Trialkoxysilane Exchange: Scope,Mechanism, Cryptates and pH-Response

The dynamic covalent chemistry (DCvC) of the Si−O bond holds unique opportunities, but has rarely been employed to assemble discrete molecular architectures. This may be due to the harsh conditions required to initiate exchange reactions at silicon in aprotic solvents. Herein, we provide a comprehensive experimental and computational account on the reaction of trialkoxysilanes with alcohols and identify mild conditions for rapid exchange in aprotic solvents. Substituent, solvent and salt effects are uncovered, understood and exploited for the construction of sila-orthoester cryptates. A sharp, divergent pH-response of the obtained cages renders this substance class attractive for future applications well beyond host-guest chemistry, for instance, in drug delivery.     

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.     

Integrating virtual screening and combinatorial chemistry for accelerated drug discovery

Virtual screening is increasingly being used in drug discovery programs with a growing number of successful applications. Experimental methodologies developed to speed up the drug discovery processes include high-throughput screening and combinatorial chemistry. The complementarities between computational and experimental screenings have been recognized and reviewed in the literature. Computational methods have also been used in the combinatorial chemistry field, in particular in library design. However, the integration of computational and combinatorial chemistry screenings has been attempted only recently. Combinatorial libraries (experimental or virtual) represent a notable source of chemically related compounds. Advances in combinatorial chemistry and deconvolution strategies, have enabled the rapid exploration of novel and dense regions in the chemical space. The present review is focused on the integration of virtual and experimental screening of combinatorial libraries. Applications of virtual screening to discover novel anticancer agents and our ongoing efforts towards the integration of virtual screening and combinatorial chemistry are also discussed.