The chemist as astronaut: Searching for biologically useful space in the chemical universe

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

First-in-class and best-in-class dendrimer nanoplatforms from concept to clinic: Lessons learned moving forward

Research to develop active dendrimers by themselves or as nanocarriers represents a promising approach to discover new biologically active entities that can be used to tackle unmet medical needs including difficult diseases. These developments are possible due to the exceptional physicochemical properties of dendrimers, including their biocompatibility, as well as their therapeutic activity as nanocarriers and drugs themselves. Despite a large number of academic studies, very few dendrimers have crossed the 'valley of death' between. Only a few number of pharmaceutical companies have succeeded in this way. In fact, only Starpharma (Australia) and Orpheris, Inc. (USA), an Ashvattha Therapeutics subsidiary, can fill all the clinic requirements to have in the market dendrimers based drugs/nancocarriers. After evaluating the main physicochemical properties related to the respective biological activity of dendrimers classified as first-in-class or best-in-class in nanomedicine, this original review analyzes the advantages and disavantages of these two strategies as well the concerns to step in clinical phases. Various solutions are proposed to advance the use of dendrimers in human health.

Following Ramachandran 2: exit vector plot (EVP) analysis of disubstituted saturated rings

EVP analysis of common saturated rings revealed five regions (α–ε); only part of them corresponds to 3D molecular structures.

Toward Small-Molecule Inhibition of Protein-Protein Interactions: General Aspects and Recent Progress in Targeting Costimulatory and Coinhibitory (Immune Checkpoint) Interactions

Protein-Protein Interactions (PPIs) that are part of the costimulatory and coinhibitory (immune checkpoint) signaling are critical for adequate T cell response and are important therapeutic targets for immunomodulation. Biologics targeting them have already achieved considerable clinical success in the treatment of autoimmune diseases or transplant recipients (e.g., abatacept, belatacept, and belimumab) as well as cancer (e.g., ipilimumab, nivolumab, pembrolizumab, atezolizumab, durvalumab, and avelumab). In view of such progress, there have been only relatively limited efforts toward developing small-molecule PPI inhibitors (SMPPIIs) targeting these cosignaling interactions, possibly because they, as all other PPIs, are difficult to target by small molecules and were not considered druggable. Nevertheless, substantial progress has been achieved during the last decade. SMPPIIs proving the feasibility of such approaches have been identified through various strategies for a number of cosignaling interactions including CD40-CD40L, OX40-OX40L, BAFFR-BAFF, CD80-CD28, and PD-1-PD-L1s. Here, after an overview of the general aspects and challenges of SMPPII-focused drug discovery, we review them briefly together with relevant structural, immune-signaling, physicochemical, and medicinal chemistry aspects. While so far only a few of these SMPPIIs have shown activity in animal models (DRI-C21045 for CD40-D40L, KR33426 for BAFFR-BAFF) or reached clinical development (RhuDex for CD80-CD28, CA-170 for PD-1-PD-L1), there is proof-of-principle evidence for the feasibility of such approaches in immunomodulation. They can result in products that are easier to develop/ manufacture and are less likely to be immunogenic or encounter postmarket safety events than corresponding biologics, and, contrary to them, can even become orally bioavailable.

Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions

The rule of five (Ro5), based on physicochemical profiles of phase II drugs, is consistent with structural limitations in protein targets and the drug target ligands. Three of four parameters in Ro5 are fundamental to the structure of both target and drug binding sites. The chemical structure of the drug ligand depends on the ligand chemistry and design philosophy. Two extremes of chemical structure and design philosophy exist; ligands constructed in the medicinal chemistry synthesis laboratory without input from natural selection and natural product (NP) metabolites biosynthesized based on evolutionary selection. Exceptions to Ro5 are found mostly among NPs. Chemistry chameleon-like behavior of some NPs due to intra-molecular hydrogen bonding as exemplified by cyclosporine A is a strong contributor to NP Ro5 outliers. The fragment derived, drug Navitoclax is an example of the extensive expertise, resources, time and key decisions required for the rare discovery of a non-NP Ro5 outlier.

Following Ramachandran: exit vector plots (EVP) as a tool to navigate chemical space covered by 3D bifunctional scaffolds. The case of cycloalkanes

An approach to analysis and visualization of chemical space covered by disubstituted scaffolds, which is based on exit vector plots (EVP), is used for analysis of cycloalkane. Four clearly defined regions (α, β, γ and δ) are found in their EVP.

Network measures for chemical library design

In this overview, we examine recent developments in network approaches to drug design. A brief overview of networks is followed by a discussion of how chemical similarity networks and their properties address challenges in drug design. Multiple methods used to assess or enhance chemical diversity for early-stage drug discovery are discussed, as well as methods that can be used for drug repositioning and ligand polypharmacology.

The applicability of molecular descriptors for designing an electrospray ionization mass spectrometry compatible library for drug discovery

Detecting a small molecular-weight compound by electrospray ionization mass spectrometry (ESI-MS) requires the compound to obtain a charge. Factors such as gas-phase proton affinities and analyte surface activity are correlated with a positive ESI-MS response, but unfortunately it is extremely challenging to predict from a chemical structure alone if a compound is likely to yield an observable molecular-ion peak in an ESI-MS spectrum. Thus, the design of a chemical library for an ESI-MS ligand-affinity screen is particularly daunting. Only 56.9% of the compounds from our FAST-NMR functional library [1] were detectable by ESI-MS. An analysis of ~1,600 molecular descriptors did not identify any correlation with a positive ESI-MS response that cannot be attributed to a skewed population distribution. Unfortunately, our results suggest that molecular descriptors are not a valuable approach for designing a chemical library for an MS-based ligand affinity screen.

Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds

The "small molecule universe" (SMU), the set of all synthetically feasible organic molecules of 500 Da molecular weight or less, is estimated to contain over 10(60) structures, making exhaustive searches for structures of interest impractical. Here, we describe the construction of a "representative universal library" spanning the SMU that samples the full extent of feasible small molecule chemistries. This library was generated using the newly developed Algorithm for Chemical Space Exploration with Stochastic Search (ACSESS). ACSESS makes two important contributions to chemical space exploration: it allows the systematic search of the unexplored regions of the small molecule universe, and it facilitates the mining of chemical libraries that do not yet exist, providing a near-infinite source of diverse novel compounds.

Intramolecular C-H···O hydrogen bonding in 1,4-dihydropyridine derivatives

The diastereotopy of the methylene protons at positions 2 and 6 in 1,4-dihydropiridine derivatives with various substituents has been investigated. NMR spectroscopy and quantum chemistry calculations show that the CH···O intramolecular hydrogen bond is one of the factors amplifying the chemical shift differences in the 1H-NMR spectra.

Recent trends and observations in the design of high-quality screening collections

The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.

Use of pyridinium ionic liquids as catalysts for the synthesis of 3,5-bis(dodecyloxycarbonyl)-1,4-dihydropyridine derivative

The synthesis of cationic amphiphilic 1,4-dihydropyridine derivative, potential gene delivery agent is achieved via an efficient multi-step sequence. The key step of this approach is a two-component Hantzsch type cyclisation of 3-oxo-2-[1-phenylmethylidene]-butyric acid dodecyl ester and 3-amino-but-2-enoic acid dodecyl ester utilising bis(2-hydroxyethyl)ether as a solvent and 1-butyl-4-methylpyridinium chloride as a catalyst. The 1,4-dihydropyridine derivative with long alkyl ester chains at positions 3 and 5 of the 1,4-DHP ring — 3,5-bis(dodecyloxycarbonyl)-2,6-dimethyl-4-phenyl-1,4-dihydropyridine was obtained in substantially higher yield with respect to classical Hantzsch synthesis. Bromination of this compound followed by nucleophilic substitution of bromine with pyridine gave the desired cationic amphiphilic 1,4-dihydropyridine.

e-LEA3D: a computational-aided drug design web server

e-LEA3D web server integrates three complementary tools to perform computer-aided drug design based on molecular fragments. In drug discovery projects, there is a considerable interest in identifying novel and diverse molecular scaffolds to enhance chances of success. The de novo drug design tool is used to invent new ligands to optimize a user-specified scoring function. The composite scoring function includes both structure- and ligand-based evaluations. The de novo approach is an alternative to a blind virtual screening of large compound collections. A heuristic based on a genetic algorithm rapidly finds which fragments or combination of fragments fit a QSAR model or the binding site of a protein. While the approach is ideally suited for scaffold-hopping, this module also allows a scan for possible substituents to a user-specified scaffold. The second tool offers a traditional virtual screening and filtering of an uploaded library of compounds. The third module addresses the combinatorial library design that is based on a user-drawn scaffold and reactants coming, for example, from a chemical supplier. The e-LEA3D server is available at: http://bioinfo.ipmc.cnrs.fr/lea.html.

Antibiotic indole sesquiterpene alkaloid from Greenwayodendron suaveolens with a new natural product framework

High-throughput natural products chemistry methods have led to the isolation of three new (1-3) and two known indole sesquiterpene alkaloids (4, 5) from Greenwayodendron suaveolens. Their structures were determined using CapNMR and MS. Pentacyclindole (1) was determined to possess a new natural product framework. Pentacyclindole (1) and polyalthenol (4) showed activity against clinical isolates of Staphylococcus aureus with polyalthenol (4) demonstrating a MIC(90) of 4 microg/mL.

Enhancements of screening collections to address areas of unmet medical need: an industry perspective

The past 20 years have witnessed an impressive expansion of the 'drug space'; defined as the intersection of the Medicinal Chemistry space and the Biologically Active space relevant in the quest for new treatments for disease. Despite the success of known lead discovery tactics, areas of unmet medical need are often linked to challenging or novel targets and are poorly served by current screening collections. A successful strategy to fill the gaps is to diversify the approaches taken in the enhancement of screening collections. Possible strategies include investments through proven methods, exploring areas of chemical space previously neglected (e.g. hydrophilic compounds, natural product mimics), and applying tactics to the lead discovery process that are complementary to HTS (e.g. fragment based screening or multidisciplinary team efforts to tackle new target classes).