Large-scale exploration of bioisosteric replacements on the basis of matched molecular pairs

From traditional to data-driven medicinal chemistry: a case study

Artificial intelligence (AI) and data science are beginning to impact drug discovery. It usually takes considerable time and effort until new scientific concepts or technologies make a transition from conceptual stages to practical applicability and until experience values are gathered. Especially for computational approaches, demonstrating measurable impact on drug discovery projects is not a trivial task. A pilot study at Daiichi Sankyo Company has attempted to integrate data-driven approaches into practical medicinal chemistry and quantify the impact, as reported herein. Although the organization and focal points of early-phase drug discovery naturally vary at different pharmaceutical companies, the results of this pilot study indicate the significant potential of data-driven medicinal chemistry and suggest new models for internal training of next-generation medicinal chemists. Keywords: medicinal chemistry; drug discovery; chemoinformatics; data science; data-driven R&D.

Systematic mapping of R-group space enables the generation of an R-group replacement system for medicinal chemistry

Selection of R-groups (substituents, functional groups) is of critical importance for the generation of analogues during hit-to-lead and lead optimization. In the practice of medicinal chemistry, R-group selection is mostly driven by chemical experience and intuition taking synthetic criteria into account. However, systematic analyses of substituents are currently rare. In this work, we have computationally isolated R-groups from more than 17,000 analog series comprising ∼315,000 bioactive compounds. From more than 50,000 unique substituents, frequently used R-groups were identified. For these R-groups, preferred replacements over more than 60,000 individual substitution sites were identified with the aid of a network data structure. These data provided the basis for the generation of a searchable R-group replacement system for medicinal chemistry containing replacement hierarchies for frequently used R-groups, which is made freely available as the central component of our study.

Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion

In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline.

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

"Molecular Anatomy": a new multi-dimensional hierarchical scaffold analysis tool

The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities. In this paper, we present a novel approach called "Molecular Anatomy" as a flexible and unbiased molecular scaffold-based metrics to cluster large set of compounds. We introduce a set of nine molecular representations at different abstraction levels, combined with fragmentation rules, to define a multi-dimensional network of hierarchically interconnected molecular frameworks. We demonstrate that the introduction of a flexible scaffold definition and multiple pruning rules is an effective method to identify relevant chemical moieties. This approach allows to cluster together active molecules belonging to different molecular classes, capturing most of the structure activity information, in particular when libraries containing a huge number of singletons are analyzed. We also propose a procedure to derive a network visualization that allows a full graphical representation of compounds dataset, permitting an efficient navigation in the scaffold's space and significantly contributing to perform high quality SAR analysis. The protocol is freely available as a web interface at https://ma.exscalate.eu .

Effects of Replacing Oxygenated Functionality with Fluorine on Lipophilicity

The replacement of oxygenated functionality (hydroxy and alkoxy) with a fluorine atom is a commonly used bioisosteric replacement in medicinal chemistry. In this paper, we use molecular matched-pair analysis to better understand the effects of this replacement on lipophilicity. It seems that the reduced log P of the oxygenated compound is normally dominant in determining the size of this difference. We observe that the presence of additional electron-donating groups on an aromatic ring generally increases the difference in lipophilicity between an oxygenated compound and its fluorinated analogue, while electron-withdrawing groups lead to smaller differences. Ortho-substituted compounds generally display a reduced difference in log P compared to para- and meta-substituted compounds, particularly if an ortho-substituent can form an intramolecular hydrogen bond. Hydrogen-bond acceptors remote to an aromatic ring containing fluorine/oxygen can also reduce the difference in log P between oxygen- and fluorine-substituted compounds.

Global Assessment of Substituents on the Basis of Analogue Series

While bioisosteric replacements have been extensively investigated, comprehensive analyses of R-/functional groups have thus far been rare in medicinal chemistry. We introduce a new analysis concept for the exploration of chemical substituent space that is based upon bioactive analogue series as a source. From ∼24,000 analogue series, more than 19,000 substituents were isolated that were differently distributed. A subset of ∼400 substituent fragments occurred most frequently in different structural contexts. These substituents contained well-known R-groups as well as novel structures. Substitution site-specific replacement and network analysis revealed that chemically similar substituents preferentially occurred at given sites and identified intuitive substitution pathways that can be explored for compound design. Taken together, the results of our analysis provide new insights into substituent space and identify preferred substituents on the basis of analogue series. As a part of our study, all the data reported are made freely available.

The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization

Matched Molecular Pairs (MMP) analysis is a well-established technique for Structure Activity and Property Analysis (SAR and SPR). Summarizing multiple MMPs that describe the same structural change into a single chemical transform can be a powerful tool for prediction (termed Transform from here on). This is particularly useful in the area of Absorption, Distribution, Metabolism, and Elimination (ADME) analysis that is less influenced by 3D structural binding effects. The creation of a knowledge database containing many of these Transforms across typical ADME assays promises to be a powerful approach to aid multidimensional optimization. We present a detailed workflow for the derivation of such a database. We include details of an MMP fragmentation algorithm with associated statistical summarization methods for the derivation of Transforms. This is made freely available as part of the LillyMol software package. We describe the application of this method to several ADME/Tox (Toxicity) assay data sets and highlight multiple cases where the impact of traditional medicinal chemistry Transforms is contradicted by MMP data. We also describe the internal software interface used by medicinal chemists to aid the design of new compounds via automated suggestion. This approach utilizes the matched pairs database to "suggest" improved compounds in an automated design scenario. A nonvisual script-based version of the automated suggestions code with an associated set of described chemical Transforms is also made freely available along with this paper and as part of the LillyMol software package. Finally, we contrast this knowledge database against a larger database of all MMPs derived from a 2 million compound diversity set and a subset of MMPs seen in historical discovery projects. The comparison against all transforms in the diversity collection highlights the very low coverage of the transform database as compared to all possible transforms involving 15 atom fragments. The comparison against a smaller subset of Transforms seen on internal Medicinal Chemistry projects shows better coverage of the transform database for a small set of common medicinal chemistry strategies. Within the context of all possible transforms available to a medicinal chemistry project team, the challenge remains to move beyond mere idea generation from past projects toward high quality prediction for novel ADME/Tox modulating Transforms.

BRADSHAW: a system for automated molecular design

This paper introduces BRADSHAW (Biological Response Analysis and Design System using an Heterogenous, Automated Workflow), a system for automated molecular design which integrates methods for chemical structure generation, experimental design, active learning and cheminformatics tools. The simple user interface is designed to facilitate access to large scale automated design whilst minimising software development required to introduce new algorithms, a critical requirement in what is a very fast moving field. The system embodies a philosophy of automation, best practice, experimental design and the use of both traditional cheminformatics and modern machine learning algorithms.

Hierarchical Analysis of Bioactive Matched Molecular Pairs, Encoded Chemical Transformations, and Associated Substructures

The matched molecular pair (MMP) concept has become popular to assess molecular property changes resulting from small chemical modifications and study structure-activity relationships (SARs). In this study, we further extend MMP analysis by introducing an MMP-based hierarchical analysis scheme. Specifically, we report a large-scale analysis of MMPs derived from bioactive compounds following a defined "MMP-transformation-substructure" hierarchy. This makes it also possible to categorize transformations and corresponding substructures on the basis of activity information. MMPs were systematically generated for compounds active against current pharmaceutical targets and stepwise decomposed into transformations and substructures. Surprisingly, most chemical transformations were only associated with single MMPs. Hence, the structural context of transformations was unexpectedly narrow. In addition, nearly half of all substructures were found to exclusively form single-target transformations. Taken together, the results of our analysis provide a detailed view of MMP-transformation-substructure hierarchy and further increase the knowledge base of the MMP approach.

The Necessary Nitrogen Atom: A Versatile High-Impact Design Element for Multiparameter Optimization

There is a continued desire in biomedical research to reduce the number and duration of design cycles required to optimize lead compounds into high-quality chemical probes or safe and efficacious drug candidates. The insightful application of impactful molecular design elements is one approach toward achieving this goal. The replacement of a CH group with a N atom in aromatic and heteroaromatic ring systems can have many important effects on molecular and physicochemical properties and intra- and intermolecular interactions that can translate to improved pharmacological profiles. In this Perspective, the "necessary nitrogen atom" is shown to be a versatile high-impact design element for multiparameter optimization, wherein ≥10-, 100-, or 1000-fold improvement in a variety of key pharmacological parameters can be realized.

OOMMPPAA: a tool to aid directed synthesis by the combined analysis of activity and structural data

There is an ever increasing resource in terms of both structural information and activity data for many protein targets. In this paper we describe OOMMPPAA, a novel computational tool designed to inform compound design by combining such data. OOMMPPAA uses 3D matched molecular pairs to generate 3D ligand conformations. It then identifies pharmacophoric transformations between pairs of compounds and associates them with their relevant activity changes. OOMMPPAA presents this data in an interactive application providing the user with a visual summary of important interaction regions in the context of the binding site. We present validation of the tool using openly available data for CDK2 and a GlaxoSmithKline data set for a SAM-dependent methyl-transferase. We demonstrate OOMMPPAA's application in optimizing both potency and cell permeability and use OOMMPPAA to highlight nuanced and cross-series SAR. OOMMPPAA is freely available to download at http://oommppaa.sgc.ox.ac.uk/OOMMPPAA/ .

Using matched molecular series as a predictive tool to optimize biological activity

A matched molecular series is the general form of a matched molecular pair and refers to a set of two or more molecules with the same scaffold but different R groups at the same position. We describe Matsy, a knowledge-based method that uses matched series to predict R groups likely to improve activity given an observed activity order for some R groups. We compare the Matsy predictions based on activity data from ChEMBLdb to the recommendations of the Topliss tree and carry out a large scale retrospective test to measure performance. We show that the basis for predictive success is preferred orders in matched series and that this preference is stronger for longer series. The Matsy algorithm allows medicinal chemists to integrate activity trends from diverse medicinal chemistry programs and apply them to problems of interest as a Topliss-like recommendation or as a hypothesis generator to aid compound design.

Large-scale SAR analysis

The analysis of structure–activity relationships (SARs) is a central task in medicinal chemistry. Traditionally, SAR exploration has concentrated on individual compound series. This conventional approach is complemented by large-scale SAR analysis, which puts strong emphasis on data mining and SAR visualization. This contribution reviews recent concepts for large-scale SAR analysis including numerical functions to characterize global and local SAR information content of compound data sets, alternative activity landscape representations and data mining strategies.

ChEMBL: a large-scale bioactivity database for drug discovery

ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.

Identification of target family directed bioisosteric replacements

Shown are exemplary replacements of chemical groups that are bioisosteric for individual target families.