The multiple roles of computational chemistry in fragment-based drug design

Fragment-based drug discovery (FBDD) represents a change in strategy from the screening of molecules with higher molecular weights and physical properties more akin to fully drug-like compounds, to the screening of smaller, less complex molecules. This is because it has been recognised that fragment hit molecules can be efficiently grown and optimised into leads, particularly after the binding mode to the target protein has been first determined by 3D structural elucidation, e.g. by NMR or X-ray crystallography. Several studies have shown that medicinal chemistry optimisation of an already drug-like hit or lead compound can result in a final compound with too high molecular weight and lipophilicity. The evolution of a lower molecular weight fragment hit therefore represents an attractive alternative approach to optimisation as it allows better control of compound properties. Computational chemistry can play an important role both prior to a fragment screen, in producing a target focussed fragment library, and post-screening in the evolution of a drug-like molecule from a fragment hit, both with and without the available fragment-target co-complex structure. We will review many of the current developments in the area and illustrate with some recent examples from successful FBDD discovery projects that we have conducted.     

CrystalDock: a novel approach to fragment-based drug design

We present a novel algorithm called CrystalDock that analyzes a molecular pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound experimental structures. Germane fragments from the crystallographic or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, respectively. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.     

In silico fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.     

SERAPhiC: a benchmark for in silico fragment-based drug design

Our main objective was to compile a data set of high-quality protein-fragment complexes and make it publicly available. Once assembled, the data set was challenged using docking procedures to address the following questions: (i) Can molecular docking correctly reproduce the experimentally solved structures? (ii) How thorough must the sampling be to replicate the experimental data? (iii) Can commonly used scoring functions discriminate between the native pose and other energy minima? The data set, named SERAPhiC (Selected Fragment Protein Complexes), is publicly available in a ready-to-dock format ( http://www.iit.it/en/drug-discovery-and-development/seraphic.html ). It offers computational medicinal chemists a reliable test set for both in silico protocol assessment and software development.     

Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design

In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.     

Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site

Eg5, a mitotic kinesin exclusively involved in the formation and function of the mitotic spindle has attracted interest as an anticancer drug target. Eg5 is co-crystallized with several inhibitors bound to its allosteric binding pocket. Each of these occupies a pocket formed by loop 5/helix α2 (L5/α2). Recently designed inhibitors additionally occupy a hydrophobic pocket of this site. The goal of the present study was to explore this hydrophobic pocket with our MED-SuMo fragment-based protocol, and thus discover novel chemical structures that might bind as inhibitors. The MED-SuMo software is able to compare and superimpose similar interaction surfaces upon the whole protein data bank (PDB). In a fragment-based protocol, MED-SuMo retrieves MED-Portions that encode protein-fragment binding sites and are derived from cross-mining protein-ligand structures with libraries of small molecules. Furthermore we have excluded intra-family MED-Portions derived from Eg5 ligands that occupy the hydrophobic pocket and predicted new potential ligands by hybridization that would fill simultaneously both pockets. Some of the latter having original scaffolds and substituents in the hydrophobic pocket are identified in libraries of synthetically accessible molecules by the MED-Search software. Ksenia Oguievetskaia and Laetitia Martin-Chanas contributed equally to this work.     

Epitopes based drug design for dengue virus envelope protein: A computational approach

Dengue virus (DENV) has emerged as a rapidly spreading epidemic throughout the tropical and subtropical regions around the globe. No suitable drug has been designed yet to fight against DENV, therefore, the need for safe and effective antiviral drug has become imperative. The envelope protein of DENV is responsible for mediating the fusion process between viral and host membranes. This work reports an in silico approach to target B and T cell epitopes for dengue envelope protein inhibition. A conserved region “QHGTI” in B and T cell epitopes of dengue envelope glycoprotein was confirmed to be valid for targeting by visualizing its interactions with the host cell membrane TIM-1 protein which acts as a receptor for serotype 2 and 3. A reverse pharmacophore mapping approach was used to generate a seven featured pharmacophore model on the basis of predicted epitope. This pharmacophore model as a 3D query was used to virtually screen a chemical compounds dataset “Chembridge”. A total of 1010 compounds mapped on the developed pharmacophore model. These retrieved hits were subjected to filtering via Lipinski’s rule of five, as a result 442 molecules were shortlisted for further assessment using molecular docking. Finally, 14 hits of different structural properties having interactions with the active site residues of dengue envelope glycoprotein were selected as lead candidates. These structurally diverse lead candidates have strong likelihood to act as further starting structures in the development of novel and potential drugs for the treatment of dengue fever.     

A fragment-based approach to the SAMPL3 Challenge

The success of molecular fragment-based design depends critically on the ability to make predictions of binding poses and of affinity ranking for compounds assembled by linking fragments. The SAMPL3 Challenge provides a unique opportunity to evaluate the performance of a state-of-the-art fragment-based design methodology with respect to these requirements. In this article, we present results derived from linking fragments to predict affinity and pose in the SAMPL3 Challenge. The goal is to demonstrate how incorporating different aspects of modeling protein-ligand interactions impact the accuracy of the predictions, including protein dielectric models, charged versus neutral ligands, ΔΔGs solvation energies, and induced conformational stress. The core method is based on annealing of chemical potential in a Grand Canonical Monte Carlo (GC/MC) simulation. By imposing an initially very high chemical potential and then automatically running a sequence of simulations at successively decreasing chemical potentials, the GC/MC simulation efficiently discovers statistical distributions of bound fragment locations and orientations not found reliably without the annealing. This method accounts for configurational entropy, the role of bound water molecules, and results in a prediction of all the locations on the protein that have any affinity for the fragment. Disregarding any of these factors in affinity-rank prediction leads to significantly worse correlation with experimentally-determined free energies of binding. We relate three important conclusions from this challenge as applied to GC/MC: (1) modeling neutral ligands--regardless of the charged state in the active site--produced better affinity ranking than using charged ligands, although, in both cases, the poses were almost exactly overlaid; (2) simulating explicit water molecules in the GC/MC gave better affinity and pose predictions; and (3) applying a ΔΔGs solvation correction further improved the ranking of the neutral ligands. Using the GC/MC method under a variety of parameters in the blinded SAMPL3 Challenge provided important insights to the relevant parameters and boundaries in predicting binding affinities using simulated annealing of chemical potential calculations.     

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.     

Fragment-based drug design: computational & experimental state of the art

Fragment-based screening is an emerging technology which is used as an alternative to high-throughput screening (HTS), and often in parallel. Fragment screening focuses on very small compounds. Because of their small size and simplicity, fragments exhibit a low to medium binding affinity (mM to μM) and must therefore be screened at high concentration in order to detect binding events. Since some issues are associated with high-concentration screening in biochemical assays, biophysical methods are generally employed in fragment screening campaigns. Moreover, these techniques are very sensitive and some of them can give precise information about the binding mode of fragments, which facilitates the mandatory hit-to-lead optimization. One of the main advantages of fragment-based screening is that fragment hits generally exhibit a strong binding with respect to their size, and their subsequent optimization should lead to compounds with better pharmacokinetic properties compared to molecules evolved from HTS hits. In other words, fragments are interesting starting points for drug discovery projects. Besides, the chemical space of low-complexity compounds is very limited in comparison to that of drug-like molecules, and thus easier to explore with a screening library of limited size. Furthermore, the "combinatorial explosion" effect ensures that the resulting combinations of interlinked binding fragments may cover a significant part of "drug-like" chemical space. In parallel to experimental screening, virtual screening techniques, dedicated to fragments or wider compounds, are gaining momentum in order to further reduce the number of compounds to test. This article is a review of the latest news in both experimental and in silico virtual screening in the fragment-based discovery field. Given the specificity of this journal, special attention will be given to fragment library design.     

A factorial design to optimize cell-based drug discovery analysis

Drug discovery is dependent on finding a very small number of biologically active or potent compounds among millions of compounds stored in chemical collections. Quantitative structure-activity relationships suggest that potency of a compound is highly related to that compound's chemical makeup or structure. To improve the efficiency of cell-based analysis methods for high throughput screening, where information of a compound's structure is used to predict potency, we consider a number of potentially influential factors in the cell-based approach. A fractional factorial design is implemented to evaluate the effects of these factors, and lift chart results show that the design scheme is able to find conditions that enhance hit rates.     

C–H functionalisation tolerant to polar groups could transform fragment-based drug discovery (FBDD)

We have analysed 131 fragment-to-lead (F2L) examples targeting a wide variety of protein families published by academic and industrial laboratories between 2015–2019. Our assessment of X-ray structural data identifies the most common polar functional groups involved in fragment-protein binding are: N–H (hydrogen bond donors on aromatic and aliphatic N–H, amides and anilines; totalling 35%), aromatic nitrogen atoms (hydrogen bond acceptors; totalling 23%), and carbonyl oxygen group atoms (hydrogen bond acceptors on amides, ureas and ketones; totalling 22%). Furthermore, the elaboration of each fragment into its corresponding lead is analysed to identify the nominal synthetic growth vectors. In ∼80% of cases, growth originates from an aromatic or aliphatic carbon on the fragment and more than 50% of the total bonds formed are carbon–carbon bonds. This analysis reveals that growth from carbocentric vectors is key and therefore robust C–H functionalisation methods that tolerate the innate polar functionality on fragments could transform fragment-based drug discovery (FBDD). As a further resource to the community, we have provided the full data of our analysis as well as an online overlay page of the X-ray structures of the fragment hit and leads: https://astx.com/interactive/F2L-2021/

An in depth meta analysis of 131 fragment-to-lead case-studies has shown the importance of synthetic methods that allow carbon-centred synthetic elaboration in the presence of polar pharmacophores.     

Recent progress in computational approaches to studying the M2 proton channel and its implication to drug design against influenza viruses

For quite a long period of time in history, many intense efforts have been made to determine the 3D (three-dimensional) structure of the M2 proton channel. The reason why the M2 proton channel has attracted so many attentions is because (1) it is the key for really understanding the life cycle of influenza viruses, and (2) it is indispensable for conducting rational drug design against the flu viruses. Recently, the long-sough 3D structures of the M2 proton channels for both influenza A and B viruses were consecutively successfully determined by the high-resolution NMR spectroscopy (Schnell J.R. and Chou, J.J., Nature, 2008, 451: 591-595; Wang, J., Pielak, R.M., McClintock, M.A., and Chou, J.J., Nature Structural & Molecular Biology, 2009,16: 1267-1271). Such a milestone work has provided a solid structural basis for in-depth understanding the action mechanism of the M2 channel and rationally designing effective drugs against influenza viruses. This review is devoted to, with the focus on the M2 proton channel of influenza A, addressing a series of relevant problems, such as how to correctly understand the novel allosteric inhibition mechanism inferred from the NMR structure that is completely different from the traditional view, what the possible impacts are to the previous functional studies in this area, and what kind of new strategy can be stimulated for drug development against influenza.     

A scalable approach to combinatorial library design for drug discovery

In this paper, we propose an algorithm for the design of lead generation libraries required in combinatorial drug discovery. This algorithm addresses simultaneously the two key criteria of diversity and representativeness of compounds in the resulting library and is computationally efficient when applied to a large class of lead generation design problems. At the same time, additional constraints on experimental resources are also incorporated in the framework presented in this paper. A computationally efficient scalable algorithm is developed, where the ability of the deterministic annealing algorithm to identify clusters is exploited to truncate computations over the entire data set to computations over individual clusters. An analysis of this algorithm quantifies the tradeoff between the error due to truncation and computational effort. Results applied on test data sets corroborate the analysis and show improvement by factors as large as 10 or more, depending on the data sets.     

Bottom-up computational modeling of semi-crystalline fibers: from atomistic to continuum scale

A bottom-up computational approach involving Molecular Dynamics (MD) of silk fiber subunits and Finite Element (FE) simulations of whole spider silk fibers is presented. The approach is discussed with an emphasis on the benefits and bottlenecks of incorporating the atomistic and continuum models of crystalline and disordered domains in the fibers. The approach does not require any empirical parameters and it is applicable to similar semi-crystalline systems.     

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.     

Virtual darwinian drug design: QSAR inverse problem, virtual combinatorial chemistry, and computational screening

The generation of diversity and its further selection by an external system is a common mechanism for the evolution of the living species and for the current drug design methods. This assumption allows us to label the methods based on generation and selection of molecular diversity as "Darwinian" ones, and to distinguish them from the structure-based, structure-modulation approaches. An example of a Darwinian method is the inverse QSAR. It consists of the computational generation of candidate chemical structures and their selection according to a previously established QSAR model. New trends in the field of combinatorial chemical syntheses comprise the concepts of virtual combinatorial synthesis and virtual or computational screening. Virtual combinatorial synthesis, closely related to inverse QSAR, can be defined as the computational simulation of the generation of new chemical structures by using a combinatorial strategy to generate a virtual library. Virtual screening is the selection of chemical structures having potential desirable properties from a database or virtual library in order to be synthesized and assayed. This review is mainly focused on graph theoretical drug design approaches, but a survey with key references is provided that covers other simulation methods.     

A combined semiempirical and DFT computational protocol for studying bioorganometallic complexes: Application to molybdocene–cysteine complexes

Computational methods can help in the design of new bioorganometallic compounds. However, the presence of multihapto or σ/π metal‐ligand bonding still precludes the direct application of either pure molecular mechanics (MM) or hybrid quantum mechanics‐MM methods to study the flexibility of biomolecules in complex with organometallics. Herein, we present a computational protocol aimed to the evaluation of the relative free energies of bioorganometallic compounds, which explores the conformational space by means of Molecular Dynamics simulations using the semiempirical PM6 method coupled with the COnductor‐like Screening MOdel solvation model followed by density functional theory (DFT) calculations including the DFT‐D3 dispersion energy correction on the most stable conformers. This protocol is applied to investigate the complexes formed between cysteine and the molybdocene entity Cp₂Mo²⁺ (Cp?η⁵? C₅H₅), which has received considerable attention due to its potential antitumor activity and chemical reactivity. The calculated structures and free energies are in agreement with those of the experimentally detected molybdocene‐cysteine adducts and allow us to investigate the reaction mechanism for the formation of the most stable 1:1 and 1:2 adducts. © 2013 Wiley Periodicals, Inc.