Mechanistically transparent models for predicting aqueous solubility of rigid, slightly flexible, and very flexible drugs (MW<2000) Accuracy near that of random forest regression

Yalkowsky's General Solubility Equation (GSE), with its three fixed constants, is popular and easy to apply, but is not very accurate for polar, zwitterionic, or flexible molecules. This review examines the findings of a series of studies, where we have sought to come up with a better prediction model, by comparing the performances of the GSE to Abraham's Solvation Equation (ABSOLV), and Random Forest regression (RFR) machine-learning (ML) method. Large, well-curated aqueous intrinsic solubility databases are available. However, drugs may be sparsely distributed in chemical space, concentrated in clusters. Even a large database might overlook some regions. Test compounds from under-represented portions of space may be poorly predicted, as might be the case with the 'loose' set of 32 drugs in the Second Solubility Challenge (2020). There appears to be still a need for better coverage of drug space. Increasingly, current trends in predictions of solubility use calculated input descriptors, which may be an advantage for exploring properties of molecules yet to be synthesized. The risk may be that overall prediction approaches might be based on accumulated uncertainty. The increasing use of ML/AI methods can lead to accurate predictions, but such predictions may not readily suggest the strategies to pursue in selecting yet-to-be-synthesized compounds. Based on our latest findings, we recommend predictions based on both 'grouped' ABSOLV(GRP) and 'Flexible Acceptor' GSE(Φ,B) models with the provided best-fit parameters, where Φ is the Kier molecular flexibility index and B is the Abraham H-bond acceptor strength. For molecules with Φ < 11, the prudent choice is to pick the Consensus Model, the average of ABSOLV(GRP) and GSE(Φ,B). For more flexible molecules, GSE(Φ,B) is recommended.

Deconstructing Markush: Improving the R&D Efficiency Using Library Selection in Early Drug Discovery

Most of the product patents claim a large number of compounds based on a Markush structure. However, the identification and optimization of new principal active ingredients is frequently driven by a simple Free Wilson approach, leading to a highly focused study only involving the chemical space nearby a hit compound. This fact raises the question: do the tested compounds described in patents really reflect the full molecular diversity described in the Markush structure? In this study, we contrast the performance of rational selection to conventional approaches in seven real-case patents, assessing their ability to describe the patent's chemical space. Results demonstrate that the integration of computer-aided library selection methods in the early stages of the drug discovery process would boost the identification of new potential hits across the chemical space.

Recent Experimental Developments in Studying Passive Membrane Transport of Drug Molecules

The ability to measure the passive membrane permeation of drug-like molecules is of fundamental biological and pharmaceutical importance. Of significance, passive diffusion across the cellular membranes plays an effective role in the delivery of many pharmaceutical agents to intracellular targets. Hence, approaches for quantitative measurement of membrane permeability have been the topics of research for decades, resulting in sophisticated biomimetic systems coupled with advanced techniques. In this review, recent developments in experimental approaches along with theoretical models for quantitative and real-time analysis of membrane transport of drug-like molecules through mimetic and living cell membranes are discussed. The focus is on time-resolved fluorescence-based, surface plasmon resonance, and second-harmonic light scattering approaches. The current understanding of how properties of the membrane and permeant affect the permeation process is discussed.

On the origins of three-dimensionality in drug-like molecules

AIM

Many medicinal chemistry-relevant structures and core scaffolds tend toward geometric planarity, which hampers the optimization of physicochemical properties desirable in drug-like molecules. As challenging drug target classes emerge, the exploitation of molecular three-dimensionality in lead optimization is becoming increasingly important. While recent interest has emphasized the importance of enhanced three-dimensionality in molecular fragment designs, the extent to which this is required in core scaffolds remains unclear.

MATERIALS & METHODS

Three computational methods, Scaffold Tree deconstruction, Synthetic Disconnection Rules retrosynthetic deconstruction and virtual library enumeration, are applied, together with the descriptors plane of best fit and principal moments of inertia, to investigate the origins of three-dimensionality in drug-like molecules.

CONCLUSION

This study informs on the stage at which molecular three-dimensionality should be considered in drug design.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

Neuropeptides: metabolism to bioactive fragments and the pharmacology of their receptors

The proteolytic processing of neuropeptides has an important regulatory function and the peptide fragments resulting from the enzymatic degradation often exert essential physiological roles. The proteolytic processing generates, not only biologically inactive fragments, but also bioactive fragments that modulate or even counteract the response of their parent peptides. Frequently, these peptide fragments interact with receptors that are not recognized by the parent peptides. This review discusses tachykinins, opioid peptides, angiotensins, bradykinins, and neuropeptide Y that are present in the central nervous system and their processing to bioactive degradation products. These well-known neuropeptide systems have been selected since they provide illustrative examples that proteolytic degradation of parent peptides can lead to bioactive metabolites with different biological activities as compared to their parent peptides. For example, substance P, dynorphin A, angiotensin I and II, bradykinin, and neuropeptide Y are all degraded to bioactive fragments with pharmacological profiles that differ considerably from those of the parent peptides. The review discusses a selection of the large number of drug-like molecules that act as agonists or antagonists at receptors of neuropeptides. It focuses in particular on the efforts to identify selective drug-like agonists and antagonists mimicking the effects of the endogenous peptide fragments formed. As exemplified in this review, many common neuropeptides are degraded to a variety of smaller fragments but many of the fragments generated have not yet been examined in detail with regard to their potential biological activities. Since these bioactive fragments contain a small number of amino acid residues, they provide an ideal starting point for the development of drug-like substances with ability to mimic the effects of the degradation products. Thus, these substances could provide a rich source of new pharmaceuticals. However, as discussed herein relatively few examples have so far been disclosed of successful attempts to create bioavailable, drug-like agonists or antagonists, starting from the structure of endogenous peptide fragments and applying procedures relying on stepwise manipulations and simplifications of the peptide structures.

Fluorous-enhanced multicomponent reactions for making drug-like library scaffolds

Multicomponent reactions (MCRs) generate multiple bonds in a single reaction process, which is highly efficient to construct relatively complex molecules. Conducting post-MCR modification reactions further increases the molecular complexity and diversity. MCR has become a powerful approach to make drug-like molecules in lead generation chemistry. In fluorous MCR (F-MCR), one of the starting materials is attached to a fluorous tag and used as the limiting agent. After the MCR, the fluorous component is fished out from the reaction mixture and used for post-MCR modifications. The fluorous tag can be finally removed in traceless fashion by displacement or cyclization reactions. Unique fluorous technology such as fluorous solid-phase extraction (F-SPE) facilitates the separation process. Other techniques such as microwave irradiation and plate-to-plate SPE can also be used to make the F-MCR even more efficient. Syntheses of unique heterocyclic and natural product-like library scaffolds using Ugi/de-Boc/cyclization, MCR/Suzuki coupling, and [3+2] cycloaddition/de-tag/cyclization protocols are described in this paper.