Atom-Atom-Path similarity and Sphere Exclusion clustering: tools for prioritizing fragment hits

Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening

Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.

Identification of Macrocyclic Peptide Families from Combinatorial Libraries Containing Noncanonical Amino Acids Using Cheminformatics and Bioinformatics Inspired Clustering

In the past decade, macrocyclic peptides gained increasing interest as a new therapeutic modality to tackle intracellular and extracellular therapeutic targets that had been previously classified as "undruggable". Several technological advances have made discovering macrocyclic peptides against these targets possible: 1) the inclusion of noncanonical amino acids (NCAAs) into mRNA display, 2) increased availability of next generation sequencing (NGS), and 3) improvements in rapid peptide synthesis platforms. This type of directed-evolution based screening can produce large numbers of potential hit sequences given that DNA sequencing is the functional output of this platform. The current standard for selecting hit peptides from these selections for downstream follow-up relies on the frequency counting and sorting of unique peptide sequences which can result in the generation of false negatives due to technical reasons including low translation efficiency or other experimental factors. To overcome our inability to detect weakly enriched peptide sequences among our large data sets, we wanted to develop a clustering method that would enable the identification of peptide families. Unfortunately, utilizing traditional clustering algorithms, such as ClustalW, is not possible for this technology due to the incorporation of NCAAs in these libraries. Therefore, we developed a new atomistic clustering method with a Pairwise Aligned Peptide (PAP) chemical similarity metric to perform sequence alignments and identify macrocyclic peptide families. With this method, low enriched peptides, including isolated sequences (singletons), can now be clustered into families providing a comprehensive analysis of NGS data resulting from macrocycle discovery selections. Additionally, upon identification of a hit peptide with the desired activity, this clustering algorithm can be used to identify derivatives from the initial data set for structure-activity relationship (SAR) analysis without requiring additional selection experiments.

A Multifaceted Hit-Finding Approach Reveals Novel LC3 Family Ligands

Autophagy-related proteins (Atgs) drive the lysosome-mediated degradation pathway, autophagy, to enable the clearance of dysfunctional cellular components and maintain homeostasis. In humans, this process is driven by the mammalian Atg8 (mAtg8) family of proteins comprising the LC3 and GABARAP subfamilies. The mAtg8 proteins play essential roles in the formation and maturation of autophagosomes and the capture of specific cargo through binding to the conserved LC3-interacting region (LIR) sequence within target proteins. Modulation of interactions of mAtg8 with its target proteins via small-molecule ligands would enable further interrogation of their function. Here we describe unbiased fragment and DNA-encoded library (DEL) screening approaches for discovering LC3 small-molecule ligands. Both strategies resulted in compounds that bind to LC3, with the fragment hits favoring a conserved hydrophobic pocket in mATG8 proteins, as detailed by LC3A-fragment complex crystal structures. Our findings demonstrate that the malleable LIR-binding surface can be readily targeted by fragments; however, rational design of additional interactions to drive increased affinity proved challenging. DEL libraries, which combine small, fragment-like building blocks into larger scaffolds, yielded higher-affinity binders and revealed an unexpected potential for reversible, covalent ligands. Moreover, DEL hits identified possible vectors for synthesizing fluorescent probes or bivalent molecules for engineering autophagic degradation of specific targets.

Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions

Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within Integrated approaches to testing and assessment (IATA).

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery

BACKGROUND

Analyzing files containing chemical information is at the core of cheminformatics. Each analysis may require a unique workflow. This paper describes the chemalot and chemalot_knime open source packages. Chemalot is a set of command line programs with a wide range of functionalities for cheminformatics. The chemalot_knime package allows command line programs that read and write SD files from stdin and to stdout to be wrapped into KNIME nodes. The combination of chemalot and chemalot_knime not only facilitates the compilation and maintenance of sequences of command line programs but also allows KNIME workflows to take advantage of the compute power of a LINUX cluster.

RESULTS

Use of the command line programs is demonstrated in three different workflow examples: (1) A workflow to create a data file with project-relevant data for structure-activity or property analysis and other type of investigations, (2) The creation of a quantitative structure-property-relationship model using the command line programs via KNIME nodes, and (3) The analysis of strain energy in small molecule ligand conformations from the Protein Data Bank database.

CONCLUSIONS

The chemalot and chemalot_knime packages provide lightweight and powerful tools for many tasks in cheminformatics. They are easily integrated with other open source and commercial command line tools and can be combined to build new and even more powerful tools. The chemalot_knime package facilitates the generation and maintenance of user-defined command line workflows, taking advantage of the graphical design capabilities in KNIME. Graphical abstract Example KNIME workflow with chemalot nodes and the corresponding command line pipe.

How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity

Background

Hierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This situation is usually disregarded and conclusions are based on a single result, leading to questions concerning the permanence of clusters in all the resulting dendrograms; this happens, for example, when using HCA for grouping molecular descriptors to select that less similar ones in QSAR studies.

Results

Representing dendrograms in graph theoretical terms allowed us to introduce four measures of cluster frequency in a canonical way, and use them to calculate cluster frequencies over the set of all possible dendrograms, taking all ties in proximity into account. A toy example of well separated clusters was used, as well as a set of 1666 molecular descriptors calculated for a group of molecules having hepatotoxic activity to show how our functions may be used for studying the effect of ties in HCA analysis. Such functions were not restricted to the tie case; the possibility of using them to derive cluster stability measurements on arbitrary sets of dendrograms having the same leaves is discussed, e.g. dendrograms from variations of HCA parameters. It was found that ties occurred frequently, some yielding tens of thousands of dendrograms, even for small data sets.

Conclusions

Our approach was able to detect trends in clustering patterns by offering a simple way of measuring their frequency, which is often very low. This would imply, that inferences and models based on descriptor classifications (e.g. QSAR) are likely to be biased, thereby requiring an assessment of their reliability. Moreover, any classification of molecular descriptors is likely to be far from unique. Our results highlight the need for evaluating the effect of ties on clustering patterns before classification results can be used accurately.Graphical abstract