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

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

Artificial intelligence-powered discovery of small molecules inhibiting CTLA-4 in cancer

BACKGROUND/OBJECTIVES

Checkpoint inhibitors, which generate durable responses in many cancer patients, have revolutionized cancer immunotherapy. However, their therapeutic efficacy is limited, and immune-related adverse events are severe, especially for monoclonal antibody treatment directed against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with the B7 proteins CD80 and CD86. Small molecules impairing the CTLA-4/CD80 interaction have been developed; however, they directly target CD80, not CTLA-4.

SUBJECTS/METHODS

In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated the hits molecules with biochemical, biophysical, immunological, and experimental animal assays.

RESULTS

The primary hits obtained from the virtual screening were successfully validated in vitro and in vivo. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4.

CONCLUSIONS

Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. Our findings support using AI-based frameworks to design small molecules targeting immune checkpoints for cancer therapy.

Design, synthesis, biological evaluation and molecular dynamics simulation studies of (R)-5-methylthiazolidin-4-One derivatives as megakaryocyte protein tyrosine phosphatase 2 (PTP-MEG2) inhibitors for the treatment of type 2 diabetes

PTP-MEG2 plays a significant role in insulin production and is able to enhance insulin signaling and improve insulin sensitivity. So, PTP-MEG2 inhibitors are closely associated with type 2 diabetes therapy. A series of novel (R)-5-methylthiazolidin-4-one derivatives were designed and synthesized, and their PTP-MEG2 inhibitory activities (IC₅₀) were determined. Among the desired compounds, 1h shares the highest inhibitory activity (IC₅₀ = 1.34 μM) against PTP-MEG2. Additionally, various post-dynamic analyses confirmed that when compound 1h bound to the PTP-MEG2, the protein conformations became unstable and the function of the pTyr recognition loop (Asn331-Cys338) would be disturbed. And thus, the ideal conformations needed for the catalytic activity was difficult to be maintained. In brief, these might be how the compound 1h worked. Furthermore, we also found that the key residues Arg332 would play a critical role in disturbing the residue interactions. AbbreviationsDCCMdynamic cross-correlation mappingDMFN,N-dimethylformamideDSSPdefinition of secondary structure of proteinsFOXOforkhead transcription factorsMDmolecular dynamicsPCAprincipal component analysisPDBprotein data bankPTKsprotein tyrosine kinasesPTPsprotein tyrosine phosphatasesPTP-MEG2megakaryocyte protein tyrosine phosphatase 2RINresidue interaction networkRINGResidue Interaction Network GeneratorRMSDroot means square deviationRMSFroot mean square fluctuationCommunicated by Ramaswamy H. Sarma.

Fragment-to-Lead Medicinal Chemistry Publications in 2017

This Miniperspective is the third in a series reviewing fragment-to-lead publications from a given year. Following our reviews for 2015 and 2016, this Miniperspective provides tabulated summaries of relevant articles published in 2017 along with some general observations. In addition, we discuss insights obtained from analysis of the combined data set of 85 examples from all three years of publications.

Chemistry-driven Hit-to-lead Optimization Guided by Structure-based Approaches

For several decades, hit identification for drug discovery has been facilitated by developments in both fragment-based and high-throughput screening technologies. However, a major bottleneck in drug discovery projects continues to be the optimization of primary hits from screening campaigns in order to derive lead compounds. Computational chemistry or molecular modeling can play an important role during this hit-to-lead (H2L) stage by both suggesting putative optimizations and decreasing the number of compounds to be experimentally synthesized and evaluated. However, it is also crucial to consider the feasibility of organically synthesizing these virtually designed compounds. Furthermore, the generated molecules should have reasonable physicochemical properties and be medicinally relevant. This review focuses on chemistry-driven and structure-based computational methods that can be used to tackle the difficult problem of H2L optimization, with emphasis being placed on the strategy developed in our laboratory.

Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases

Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs.

Fragment-assisted hit investigation involving integrated HTS and fragment screening: Application to the identification of phosphodiesterase 10A (PDE10A) inhibitors

Fragment-based drug design (FBDD) relies on direct elaboration of fragment hits and typically requires high resolution structural information to guide optimization. In fragment-assisted drug discovery (FADD), fragments provide information to guide selection and design but do not serve as starting points for elaboration. We describe FADD and high-throughput screening (HTS) campaign strategies conducted in parallel against PDE10A where fragment hit co-crystallography was not available. The fragment screen led to prioritized fragment hits (IC50's ∼500μM), which were used to generate a hypothetical core scaffold. Application of this scaffold as a filter to HTS output afforded a 4μM hit, which, after preparation of a small number of analogs, was elaborated into a 16nM lead. This approach highlights the strength of FADD, as fragment methods were applied despite the absence of co-crystallographical information to efficiently identify a lead compound for further optimization.

De novo design - hop(p)ing against hope

Current trends in computational de novo design provide a fresh approach to 'scaffold-hopping' in drug discovery. The methodological repertoire is no longer limited to receptor-based methods, but specifically ligand-based techniques that consider multiple properties in parallel, including the synthetic feasibility of the computer-generated molecules and their polypharmacology, provide innovative ideas for the discovery of new chemical entities. The concept of fragment-based and virtual reaction-driven design enables rapid compound optimization from scratch with a manageable complexity of the search. Starting from known drugs as a reference, such algorithms suggest drug-like molecules with motivated scaffold variations, and advanced mathematical models of structure-activity landscapes and multi-objective design techniques have created new opportunities for hit and lead finding.

Future De Novo Drug Design

The computer-assisted generation of new chemical entities (NCEs) has matured into solid technology supporting early drug discovery. Both ligand- and receptor-based methods are increasingly used for designing small lead- and druglike molecules with anticipated multi-target activities. Advanced "polypharmacology" prediction tools are essential pillars of these endeavors. In addition, it has been realized that iterative design-synthesis-test cycles facilitate the rapid identification of NCEs with the desired activity profile. Lab-on-a-chip platforms integrating synthesis, analytics and bioactivity determination and controlled by adaptive, chemistry-driven de novo design software will play an important role for future drug discovery.

Virtual and biophysical screening targeting the γ-tubulin complex--a new target for the inhibition of microtubule nucleation

Microtubules are the main constituents of mitotic spindles. They are nucleated in large amounts during spindle assembly, from multiprotein complexes containing γ-tubulin and associated γ-tubulin complex proteins (GCPs). With the aim of developing anti-cancer drugs targeting these nucleating complexes, we analyzed the interface between GCP4 and γ-tubulin proteins usually located in a multiprotein complex named γ-TuRC (γ-Tubulin Ring Complex). 10 ns molecular dynamics simulations were performed on the heterodimers to obtain a stable complex in silico and to analyze the residues involved in persistent protein-protein contacts, responsible for the stability of the complex. We demonstrated in silico the existence of a binding pocket at the interface between the two proteins upon complex formation. By combining virtual screening using a fragment-based approach and biophysical screening, we found several small molecules that bind specifically to this pocket. Sub-millimolar fragments have been experimentally characterized on recombinant proteins using differential scanning fluorimetry (DSF) for validation of these compounds as inhibitors. These results open a new avenue for drug development against microtubule-nucleating γ-tubulin complexes.

Three- and four-body corrected fragment molecular orbital calculations with a novel subdividing fragmentation method applicable to structure-based drug design

We develop an inter-fragment interaction energy (IFIE) analysis based on the three- and four-body corrected fragment molecular orbital (FMO3 and FMO4) method to evaluate the interactions of functional group units in structure-based drug design context. The novel subdividing fragmentation method for a ligand (in units of their functional groups) and amino acid residues (in units of their main and side chains) enables us to understand the ligand-binding mechanism in more detail without sacrificing chemical accuracy of the total energy and IFIEs by using the FMO4 method. We perform FMO4 calculations with the second order Møller-Plesset perturbation theory for an estrogen receptor (ER) and the 17β-estradiol (EST) complex using the proposed fragmentation method and assess the interaction for each ligand-binding site by the FMO4-IFIE analysis. When the steroidal EST is divided into two functional units including "A ring" and "D ring", respectively, the FMO4-IFIE analysis reveals their binding affinity with surrounding fragments of the amino acid residues; the "A ring" of EST has polarization interaction with the main chain of Thr347 and two hydrogen bonds with the side chains of Glu353 and Arg394; the "D ring" of EST has a hydrogen bond with the side chain of His524. In particular, the CH/π interactions of the "A ring" of EST with the side chains of Leu387 and Phe404 are easily identified in cooperation with the CHPI program. The FMO4-IFIE analysis using our novel subdividing fragmentation method, which provides higher resolution than the conventional IFIE analysis in units of ligand and each amino acid reside in the framework of two-body approximation, is a useful tool for revealing ligand-binding mechanism and would be applicable to rational drug design such as structure-based drug design and fragment-based drug design.

Discovery of 4-hydroxy-1,6-naphthyridine-3-carbonitrile derivatives as novel PDE10A inhibitors

A series of 1,6-naphthyridine-based compounds was synthesized as potent phosphodiesterase 10A (PDE10A) inhibitors. Structure-based chemical modifications of the discovered chemotype served to further improve potency and selectivity over DHODH, laying the foundation for future optimization efforts.

Principles of early drug discovery

Developing a new drug from original idea to the launch of a finished product is a complex process which can take 12-15 years and cost in excess of $1 billion. The idea for a target can come from a variety of sources including academic and clinical research and from the commercial sector. It may take many years to build up a body of supporting evidence before selecting a target for a costly drug discovery programme. Once a target has been chosen, the pharmaceutical industry and more recently some academic centres have streamlined a number of early processes to identify molecules which possess suitable characteristics to make acceptable drugs. This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development, high throughput screening, hit identification, lead optimization and finally the selection of a candidate molecule for clinical development.

The role of fragment-based and computational methods in polypharmacology

Polypharmacology-based strategies are gaining increased attention as a novel approach to obtaining potentially innovative medicines for multifactorial diseases. However, some within the pharmaceutical community have resisted these strategies because they can be resource-hungry in the early stages of the drug discovery process. Here, we report on fragment-based and computational methods that might accelerate and optimize the discovery of multitarget drugs. In particular, we illustrate that fragment-based approaches can be particularly suited for polypharmacology, owing to the inherent promiscuous nature of fragments. In parallel, we explain how computer-assisted protocols can provide invaluable insights into how to unveil compounds theoretically able to bind to more than one protein. Furthermore, several pragmatic aspects related to the use of these approaches are covered, thus offering the reader practical insights on multitarget-oriented drug discovery projects.

Small-molecule inhibitors of IL-2/IL-2R: lessons learned and applied

The IL-2:IL-2R protein-protein interaction is of central importance to both healthy and diseased immune responses, and is one of the earliest examples of successful small-molecule inhibitor discovery against this target class. Drug-like inhibitors of IL-2 have been identified through a combination of fragment discovery, structure-based design, and medicinal chemistry; this discovery approach illustrates the importance of using a diverse range of complementary screening methods and analytical tools to achieve a comprehensive understanding of molecular recognition. The IL-2 story also provides insight into the dynamic nature of protein-protein interaction surfaces, their potential druggability, and the physical and chemical properties of effective small-molecule ligands. These lessons, from IL-2 and similar discovery programs, underscore an increasing awareness of the principles governing the development of drugs for protein-protein interactions.

The role of computational methods in the identification of bioactive compounds

Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.

Fragment-based approaches and computer-aided drug discovery

Fragment-based design has significantly modified drug discovery strategies and paradigms in the last decade. Besides technological advances and novel therapeutic avenues, one of the most significant changes brought by this new discipline has occurred in the minds of drug designers. Fragment-based approaches have markedly impacted rational computer-aided design both in method development and in applications. The present review illustrates the importance of molecular fragments in many aspects of rational ligand design, and discusses how thinking in "fragment space" has boosted computational biology and chemistry.

Methods for combinatorial and parallel library design

Diversity has historically played a critical role in design of combinatorial libraries, screening sets and corporate collections for lead discovery. Large library design dominated the field in the 1990s with methods ranging anywhere from purely arbitrary through property based reagent selection to product based approaches. In recent years, however, there has been a downward trend in library size. This was due to increased information about the desirable targets gleaned from the genomics revolution and to the ever growing availability of target protein structures from crystallography and homology modeling. Creation of libraries directed toward families of receptors such as GPCRs, kinases, nuclear hormone receptors, proteases, etc., replaced the generation of libraries based primarily on diversity while single target focused library design has remained an important objective. Concurrently, computing grids and cpu clusters have facilitated the development of structure based tools that screen hundreds of thousands of molecules. Smaller "smarter" combinatorial and focused parallel libraries replaced those early un-focused large libraries in the twenty-first century drug design paradigm. While diversity still plays a role in lead discovery, the focus of current library design methods has shifted to receptor based methods, scaffold hopping/bio-isostere searching, and a much needed emphasis on synthetic feasibility. Methods such as "privileged substructures based design" and pharmacophore based design still are important methods for parallel and small combinatorial library design. This chapter discusses some of the possible design methods and presents examples where they are available.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

Introduction to fragment-based drug discovery

Fragment-based drug discovery (FBDD) has emerged in the past decade as a powerful tool for discovering drug leads. The approach first identifies starting points: very small molecules (fragments) that are about half the size of typical drugs. These fragments are then expanded or linked together to generate drug leads. Although the origins of the technique date back some 30 years, it was only in the mid-1990s that experimental techniques became sufficiently sensitive and rapid for the concept to be become practical. Since that time, the field has exploded: FBDD has played a role in discovery of at least 18 drugs that have entered the clinic, and practitioners of FBDD can be found throughout the world in both academia and industry. Literally dozens of reviews have been published on various aspects of FBDD or on the field as a whole, as have three books (Jahnke and Erlanson, Fragment-based approaches in drug discovery, 2006; Zartler and Shapiro, Fragment-based drug discovery: a practical approach, 2008; Kuo, Fragment based drug design: tools, practical approaches, and examples, 2011). However, this chapter will assume that the reader is approaching the field with little prior knowledge. It will introduce some of the key concepts, set the stage for the chapters to follow, and demonstrate how X-ray crystallography plays a central role in fragment identification and advancement.

Mapping of ligand-binding cavities in proteins

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs.