Drug Guru: a computer software program for drug design using medicinal chemistry rules

BIreactive: Expanding the Scope of Reactivity Predictions to Propynamides

We present the first comprehensive study on the prediction of reactivity for propynamides. Covalent inhibitors like propynamides often show improved potency, selectivity, and unique pharmacologic properties compared to their non-covalent counterparts. In order to achieve this, it is essential to tune the reactivity of the warhead. This study shows how three different in silico methods can predict the in vitro properties of propynamides, a covalent warhead class integrated into approved drugs on the market. Whereas the electrophilicity index is only applicable to individual subclasses of substitutions, adduct formation and transition state energies have a good predictability for the in vitro reactivity with glutathione (GSH). In summary, the reported methods are well suited to estimate the reactivity of propynamides. With this knowledge, the fine tuning of the reactivity is possible which leads to a speed up of the design process of covalent drugs.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Towards depeptidized aminoboronic acid derivatives through the use of borylated iminium ions

Herein, we use α-boryl iminium intermediates to access progressively depeptidized branched β-aminoboronic acids that are functionalized with biologically relevant heterocycles. We investigate the interaction of these novel compounds with carbohydrates under physiological conditions and demonstrate their potential as synthetic building blocks.

Systemic evolutionary chemical space exploration for drug discovery

Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.

Computational anti-COVID-19 drug design: progress and challenges

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges

Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.

Deep scaffold hopping with multimodal transformer neural networks

Scaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecules. Traditionally, scaffolding hopping depends on searching databases of available compounds that can't exploit vast chemical space. In this study, we have re-formulated this task as a supervised molecule-to-molecule translation to generate hopped molecules novel in 2D structure but similar in 3D structure, as inspired by the fact that candidate compounds bind with their targets through 3D conformations. To efficiently train the model, we curated over 50 thousand pairs of molecules with increased bioactivity, similar 3D structure, but different 2D structure from public bioactivity database, which spanned 40 kinases commonly investigated by medicinal chemists. Moreover, we have designed a multimodal molecular transformer architecture by integrating molecular 3D conformer through a spatial graph neural network and protein sequence information through Transformer. The trained DeepHop model was shown able to generate around 70% molecules having improved bioactivity together with high 3D similarity but low 2D scaffold similarity to the template molecules. This ratio was 1.9 times higher than other state-of-the-art deep learning methods and rule- and virtual screening-based methods. Furthermore, we demonstrated that the model could generalize to new target proteins through fine-tuning with a small set of active compounds. Case studies have also shown the advantages and usefulness of DeepHop in practical scaffold hopping scenarios.

MolOpt: A Web Server for Drug Design using Bioisosteric Transformation

BACKGROUND

Bioisosteric replacement is widely used in drug design for lead optimization. However, the identification of a suitable bioisosteric group is not an easy task.

METHODS

In this work, we present MolOpt, a web server for in silico drug design using bioisosteric transformation. Potential bioisosteric transformation rules were derived from data mining, deep generative machine learning and similarity comparison. MolOpt tries to assist the medicinal chemist in his/her search for what to make next.

RESULTS AND DISCUSSION

By replacing molecular substructures with similar chemical groups, MolOpt automatically generates lists of analogues. MolOpt also evaluates forty important pharmacokinetic and toxic properties for each newly designed molecule. The transformed analogues can be assessed for possible future study.

CONCLUSION

MolOpt is useful for the identification of suitable lead optimization ideas. The MolOpt Server is freely available for use on the web at http://xundrug.cn/molopt.

Artificial intelligence in drug discovery: recent advances and future perspectives

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.

Ligand efficiency indices for effective drug discovery: a unifying vector formulation

INTRODUCTION

The area of ligand efficiency indices (LEIs) in drug discovery has developed significantly since the initial publications nearly 20 years ago. A large number of different LEIs have been defined and applied with certain degrees of success and acceptance in the community. An overall view emphasizing more the common elements than the differences is needed.

AREAS COVERED

In this review, the author accentuates the numerical and algebraic relationships among the different LEIs and proposes the notion of 'ligand efficiency index' (LEI) as a vector variable comprising two interrelated components that provide 'direction' and 'distance' along the drug discovery process. The same concept had been suggested before relating to the graphical representation of the content of Structure-Activity Databases (SAR-Databases).

EXPERT OPINION

The extension of the concept of ligand efficiency from a scalar to a vector will help to unify the different formulations by emphasizing the relationship among the different variables. It should also provide an algebraically robust framework to critically assess the value of LEIs, and to incorporate them routinely in various workflows and protocols. Only cautious and rigorous testing by the community could provide a definitive proof of their possible value as reliable optimization variables in drug discovery.

Design, synthesis and biological evaluation of novel triaryldimethylaminobutan-2-ol derivatives against Mycobacterium tuberculosis

Bedaquiline (TMC207), a typical diarylquinoline anti-tuberculosis drug, has been approved by FDA to specifically treat MDR-TB. Herein we describe design, synthesis, and in vitro biological evaluation against Mycobacterium tuberculosis of a series of triaryldimethylaminobutan-2-ol derivatives obtaining from the structural modification of TMC207. Compounds 23, 25, 28, 32, 39 and 43 provided superior anti-mycobacterial activity than positive control PC01 which shows the same configuration and contains TMC207. Compounds 16, 20, 29, 34, 37, 45 and 47 exhibited the similar activity to positive control PC01. Most importantly, the series of compounds showed excellent activity against XDR-Mtb. The result of acute toxicity suggested that this class of triaryldimethylaminobutan-2-ol derivatives should be graded as low. Further SAR analysis indicates that a large steric bulk of triaryl and 7-Br, 3-OCH₃ on 1-naphthyl are critical.

Deep SAR matrix: SAR matrix expansion for advanced analog design using deep learning architectures

Aim: Enhancing the structure–activity relationship matrix (SARM) methodology through integration of deep learning and expansion of chemical space coverage. Background: Analog design is of critical importance for medicinal chemistry. The SARM approach, which combines systematic structural organization of compound series with analog design, is put into scientific context. Methodology: The new DeepSARM concept is introduced. The architecture of SARM-integrated deep generative models is detailed and the workflow for advanced analog design and matrix expansion described. Exemplary application: The DeepSARM approach is applied to design analogs of kinase inhibitors taking kinome-wide chemical space into account. Future perspective: Practical applications of DeepSARM will be a major focal point. Different applications are discussed. New computational features will be added to prioritize virtual candidate compounds.

Capturing and applying knowledge to guide compound optimisation

Successful drug discovery requires knowledge and experience across many disciplines, and no current 'artificial intelligence' (AI) method can replace expert scientists. However, computers can recall more information than any individual or team and facilitate the transfer of knowledge across disciplines. Here, we discuss how knowledge relating to chemistry and the biological and physicochemical properties required for a successful compound can be captured. Furthermore, we illustrate how, by combining and applying this knowledge computationally, a broader range of optimisation strategies can be rigorously explored, and the results presented in an intuitive way for consideration by the experts.

Cheminformatics in the Service of GPCR Drug Discovery

Cheminformatics is a broad discipline covering a wide range of computational approaches, including the characterization of molecular similarity, pattern recognition, and predictive modeling. The unifying theme that these apparently disparate methods have in common is the aim of extracting useable information from the increasing amounts of data that are associated with contemporary drug discovery projects. Both proprietary and publically available data can be exploited to help inform and improve the process of developing novel therapeutic molecules targeting the GPCR family of proteins.

Recent applications of machine learning in medicinal chemistry

In recent decades, artificial intelligence and machine learning have played a significant role in increasing the efficiency of processes across a wide spectrum of industries. When it comes to the pharmaceutical and biotechnology sectors, numerous tools enabled by advancement of computer science have been developed and are now routinely utilized. However, there are many aspects of the drug discovery process, which can further benefit from refinement of computational methods and tools, as well as improvement of accessibility of these new technologies. In this review, examples of recent developments in machine learning application are described, which have the potential to impact different parts of the drug discovery and development flow scheme. Notably, new deep learning-based approaches across compound design and synthesis, prediction of binding, activity and ADMET properties, as well as applications of genetic algorithms are highlighted.

Compound property enhancement by virtual compound synthesis

During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds. When a user inputs a compound, the system recursively applies virtual synthetic reaction rules to the compound to improve its properties. The synthetic pathway can also be traced by the user because the reaction rules in this system are based on real organic synthesis reactions. This system has useful functions for effective drug design, such as structural preservation, allowing the substructures necessary for potency to be maintained. In this paper, to confirm the effect of directional reaction sets, we applied the reaction sets to 100 compounds. Moreover, to confirm that the system can reproduce real synthetic pathways, the synthetic pathways of Ibuprofen and Ofloxacin were explored by inputting isobutyl benzene and 7,8-difluoro-2,3-dihydro-3-methyl-4H-benzoxazine. This application is available at the following URL: http://enh.sekijima-lab.org .

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.

Flexible Analog Search with Kernel PCA Embedded Molecule Vectors

Studying analog series to find structural transformations that enhance the activity and ADME properties of lead compounds is an important part of drug development. Matched molecular pair (MMP) search is a powerful tool for analog analysis that imitates researchers' ability to select pairs of compounds that differ only by small well-defined transformations. Abstraction is a challenge for existing MMP search algorithms, which can result in the omission of relevant, inexact MMPs, and inclusion of irrelevant, contextually dissimilar MMPs. In this work, we present a new method for MMP search that returns approximate results and enables flexible control over abstraction of contextual information. We illustrate the concepts and mechanics of our method with a series of exemplar MMP queries, and then benchmark search accuracy using MMPs found by fragment indexing. We show that we can search for MMPs in a context dependent manner, and accurately approximate context independent fragment index based MMP search over a range of fingerprint and dataset conditions. Our method can be used to search for pairwise correspondences among analog sets and bolster MMP datasets where data is missing or incomplete.

Treatment of novel IL17A inhibitor in glioblastoma implementing 3rd generation co-culture cell line and patient-derived tumor model

Despite many treatment options, cancer remains a growing problem and has become the second leading cause of death globally. Here, we present fluorescence molecular tomography (FMT) data regarding the reversion of third generation co-cultured U87+DBTRG and patient-derived GBM tumor model after treatment with novel IL17A inhibitor named FLVM and FLVZ (organic derivatives of caffeic acid). FMT was used to determine tumor angiogenesis volume (assessment of number of blood vessel; the expression of angiogenic factors CD34 and other angiogenic cancer bio-markers) in U87+DBTRG and patient-derived gliomas. Immunohistochemistry was used to determine microvessel density [CD34], and cell proliferation [Ki67]. Western blot was used to assess the interleukin 17A [IL17A], vascular endothelial growth factor [VEGF] and hypoxia-inducible factor-1α [HIF-1α]. Antibody array was used to assess the cancer bio-markers in co-cultured U87+DBTRG gliomas. Animal survival was found to be significantly increased (P<0.0001) after FLVM treatment compared with control-IL17A. After FMT detection, FLVM, administered orally, was found to decrease tumor growth (P<0.0001). FLVM and FLVZ administration resulted in significant decreases in tumor hypoxia [HIF-1α (P<0.05)], angiogenesis [CD34 (P<0.05)], VEGF, IL17A and cell proliferation [Ki67 (P<0.05)] and caused a significant increase of Bax, caspase and FasL (P<0.05), compared with untreated animals. Additionally, Leptin, LPL (P<0.01), FFA (P<0.05) and adipogenesis were downregulated and no additive toxicity was found in mice except calorie-restriction like effect. Use of FLVM can be considered as a novel inhibitor of IL17A for the treatment of human gliomas.

Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations

Molecular matched pair (MMP) analysis has been used for more than 40 years within molecular design and is still an important tool to analyse potency data and other compound properties. The methods used to find matched pairs range from manual inspection, through supervised methods to unsupervised methods, which are able to find previously unknown molecular pairs. Recent publications demonstrate the value of automatic MMP analysis of publicly available bioactivity databases. The MMP concept has its limitations, but because of its easy to use and intuitive nature, it will remain one of the most important tools in the toolbox of many drug designers.

Designing the angiogenic inhibitor for brain tumor via disruption of VEGF and IL17A expression

Glioblastoma multiforme is a highly malignant, heterogenic, and drug resistant tumor. The blood-brain barrier (BBB), systemic cytotoxicity, and limited specificity are the main obstacles in designing brain tumor drugs. In this study a computational approach was used to design brain tumor drugs that could downregulate VEGF and IL17A in glioblastoma multiforme type four. Computational screening tools were used to evaluate potential candidates for antiangiogenic activity, target binding, BBB permeability, and ADME physicochemical properties. Additionally, in vitro cytotoxicity, migration, invasion, tube formation, apoptosis, ROS and ELISA assays were conducted for molecule 6 that was deemed most likely to succeed. The efflux ratio of membrane permeability and calculated docking scores of permeability to glycoproteins (P-gps) were used to determine the BBB permeability of the molecules. The results showed BBB permeation for molecule 6, with the predicted efficiency of 0.55kcal/mol and binding affinity of -37kj/mol corresponding to an experimental efflux ratio of 0.625 and predicted -15kj/mol of binding affinity for P-gps. Molecule 6 significantly affected the angiogenesis pathways by 2-fold downregulation of IL17A and VEGF through inactivation of active sites of HSP90 (predicted binding: -37kj/mol, predicted efficiency: 0.55kcal/mol) and p23 (predicted binding: 12kj/mol, predicted efficiency: 0.17kcal/mol) chaperon proteins. Additionally, molecule 6 activated the 17.38% relative fold of ROS level at 18.3μg/mL and upregulated the caspase which lead the potential synergistic apoptosis through the antiangiogenic activity of molecule 6 and thereby the highly efficacious anticancer upshot. The results indicate that the binding of the molecules to the therapeutic target is not essential to produce a lethal effect on cancer cells of the brain and that antiangiogenic efficiency is much more important.

Systematic assessment of analog relationships between bioactive compounds and promiscuity of analog sets

Compound–analog relationships. Shown is an active compound with four substitution sites, two of which are explored with four and seven different R-groups, respectively.

Using Cheminformatics in Drug Discovery

This chapter illustrates how cheminformatics can be applied to designing novel compounds that are active at the primary target and have good predicted ADMET properties. Examples of various cheminformatics techniques are illustrated in the process of designing inhibitors that inhibit both cyclooxygenase isoforms but are more potent toward COX-2. The first step in the process is to create a knowledge database of cyclooxygenase inhibitors in the public domain. This data was analyzed to find activity cliffs - small structural changes that result in drastic changes in potency. Additional cyclooxygenase potency and selectivity trends were obtained using matched molecular pair analysis. QSAR models were then developed to predict cyclooxygenase potency and selectivity. Next, computational algorithms were used to generate novel scaffolds starting from known cyclooxygenase inhibitors. Nine virtual libraries containing 240 compounds each were constructed. Predictions from the cyclooxygenase QSAR models were used to eliminate molecules with undesirable potency or selectivity. Additionally, the compounds were screened in silico for undesirable ADMET properties, e.g., low solubility, permeability, metabolic stability, or high toxicity, using a liability scoring system known as ADMET Risk™. Eight synthetic candidates were identified from this process after incorporating knowledge gained from activity cliff analysis. Four of the compounds were synthesized and tested to measure their COX-1 and COX-2 IC(50) values as well as several ADME properties. The best compound, SLP0020, had a COX-1 IC(50) of 770 nM and COX-2 IC(50) of 130 nM.

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

Current insights into computer-aided immunotherapeutic design strategies

Drug designing costs as well as design of immunotherapeutic agents could be nearly halved through the involvement of computer-aided drug designing methods in discovery and research. The inter-disciplinary, time-, and money-consuming process of drug discovery is amended by the development of drug designing, the technique of creating or finding a molecule that can render stimulatory or inhibitory activity upon various biological organisms. Meanwhile, the advancements made within this scientific domain in the last couple of decades have significantly modified and affected the way new bioactive molecules have been produced by the pharmaceutical industry. In this regard, improvements made in hardware solutions and computational techniques along with their efficient integration with biological processes have revolutionized the in silico methods in speeding up the lead identification and optimization processes. In this review, we will discuss various methods of recent computer-aided drug designing techniques that forms the basis of modern day drug discovery projects.

Alternative variables in drug discovery: promises and challenges

A number of alternative variables have appeared in the medicinal chemistry literature trying to provide a more rigorous formulation of the guidelines proposed by Lipinski to exclude chemical entities with poor pharmacokinetic properties early in the discovery process. Typically, these variables combine the affinity towards the target with physicochemical properties of the ligand and are named efficiencies or ligand efficiencies. Several formulations have been defined and used by different laboratories with different degrees of success. A unified formulation, ligand efficiency indices, was proposed that included efficiency in two complementary variables (i.e., size and polarity) to map and monitor the drug-discovery process (AtlasCBS). The use of this formulation in combination with an extended multiparameter optimization is presented, with examples, as a promising methodology to optimize the drug-discovery process in the future. Future perspectives and challenges for this approach are also discussed.

Advances in multiparameter optimization methods for de novo drug design

INTRODUCTION

A high-quality drug must achieve a balance of physicochemical and absorption, distribution, metabolism and elimination properties, safety and potency against its therapeutic target(s). Multiparameter optimization (MPO) methods guide the simultaneous optimization of multiple factors to quickly target compounds with the highest chance of downstream success. MPO can be combined with 'de novo design' methods to automatically generate and assess a large number of diverse structures and identify strategies to optimize a compound's overall balance of properties.

AREAS COVERED

The article provides a review of MPO methods and recent developments in the methods and opinions in the field. It also provides a description of advances in de novo design that improve the relevance of automatically generated compound structures and integrate MPO. Finally, the article provides discussion of a recent case study of the automatic design of ligands to polypharmacological profiles.

EXPERT OPINION

Recent developments have reduced the generation of chemically infeasible structures and improved the quality of compounds generated by de novo design methods. There are concerns about the ability of simple drug-like properties and ligand efficiency indices to effectively guide the detailed optimization of compounds. De novo design methods cannot identify a perfect compound for synthesis, but it can identify high-quality ideas for detailed consideration by an expert scientist.

Physicochemical and DMPK in silico models: facilitating their use by medicinal chemists

It is known that the developability of drugs is related to their physicochemical and DMPK properties. Given the time and expense involved in discovering and developing new drugs, maximizing the chance of success by calculating properties ahead of chemical synthesis and testing, and only acting on those candidates whose properties fall into a desired range, would seem to make sense. This paper provides an overview of calculable physicochemical and DMPK properties, an assessment of their relative difficulty of their calculation and accuracy, and available software. Methods companies have employed to communicate results will be discussed, including the use of composite scoring functions and ranking schemes. Calculations do no good if chemists will not use them to prioritize synthesis decisions. Strategies are presented for facilitating model usage. An approach adopted at Genentech for presenting results that involves the close coupling of property calculations with 3D structure based drug design is described.

Automated design of ligands to polypharmacological profiles

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

De novo design of potential RecA inhibitors using multi objective optimization

De novo ligand design involves optimization of several ligand properties such as binding affinity, ligand volume, drug likeness, etc. Therefore, optimization of these properties independently and simultaneously seems appropriate. In this paper, the ligand design problem is modeled in a multiobjective using Archived MultiObjective Simulated Annealing (AMOSA) as the underlying search algorithm. The multiple objectives considered are the energy components similarity to a known inhibitor and a novel drug likeliness measure based on Lipinski's rule of five. RecA protein of Mycobacterium tuberculosis, causative agent of tuberculosis, is taken as the target for the drug design. To gauge the goodness of the results, they are compared to the outputs of LigBuilder, NEWLEAD, and Variable genetic algorithm (VGA). The same problem has also been modeled using a well-established genetic algorithm-based multiobjective optimization technique, Nondominated Sorting Genetic Algorithm-II (NSGA-II), to find the efficacy of AMOSA through comparative analysis. Results demonstrate that while some small molecules designed by the proposed approach are remarkably similar to the known inhibitors of RecA, some new ones are discovered that may be potential candidates for novel lead molecules against tuberculosis.

Medicinal chemistry matters - a call for discipline in our discipline

Medicinal chemistry makes a vital contribution to small molecule drug discovery, and the quality of it contributes directly to research effectiveness as well as to downstream costs, speed and survival in development. In recent years, the discipline of medicinal chemistry has evolved and witnessed many noteworthy contributions that propose and offer potential improvements to medicinal chemistry practice; however, the impact of these ideas is limited by their acceptance and deployment into every-day activity and, as a result, the quality of medicinal chemistry remains variable. For the good of the industry and the medicinal chemistry discipline, there is a need to move from retrospective learning to prospective control of medicinal chemistry practice to improve cost effectiveness, probability of success and survival rates.

Applications and limitations of in silico models in drug discovery

Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.

SCRIPDB: a portal for easy access to syntheses, chemicals and reactions in patents

The patent literature is a rich catalog of biologically relevant chemicals; many public and commercial molecular databases contain the structures disclosed in patent claims. However, patents are an equally rich source of metadata about bioactive molecules, including mechanism of action, disease class, homologous experimental series, structural alternatives, or the synthetic pathways used to produce molecules of interest. Unfortunately, this metadata is discarded when chemical structures are deposited separately in databases. SCRIPDB is a chemical structure database designed to make this metadata accessible. SCRIPDB provides the full original patent text, reactions and relationships described within any individual patent, in addition to the molecular files common to structural databases. We discuss how such information is valuable in medical text mining, chemical image analysis, reaction extraction and in silico pharmaceutical lead optimization. SCRIPDB may be searched by exact chemical structure, substructure or molecular similarity and the results may be restricted to patents describing synthetic routes. SCRIPDB is available at http://dcv.uhnres.utoronto.ca/SCRIPDB.

Applying medicinal chemistry transformations and multiparameter optimization to guide the search for high-quality leads and candidates

In this article we describe a computational method that automatically generates chemically relevant compound ideas from an initial molecule, closely integrated with in silico models, and a probabilistic scoring algorithm to highlight the compound ideas most likely to satisfy a user-defined profile of required properties. The new compound ideas are generated using medicinal chemistry 'transformation rules' taken from examples in the literature. We demonstrate that the set of 206 transformations employed is generally applicable, produces a wide range of new compounds, and is representative of the types of modifications previously made to move from lead-like to drug-like compounds. Furthermore, we show that more than 94% of the compounds generated by transformation of typical drug-like molecules are acceptable to experienced medicinal chemists. Finally, we illustrate an application of our approach to the lead that ultimately led to the discovery of duloxetine, a marketed serotonin reuptake inhibitor.