Navigating large chemical spaces in early-phase drug discovery.

The size of actionable chemical spaces is surging, owing to a variety of novel techniques, both computational and experimental. As a consequence, novel molecular matter is now at our fingertips that cannot and should not be neglected in early-phase drug discovery. Huge, combinatorial, make-on-demand chemical spaces with high probability of synthetic success rise exponentially in content, generative machine learning models go hand in hand with synthesis prediction, and DNA-encoded libraries offer new ways of hit structure discovery. These technologies enable to search for new chemical matter in a much broader and deeper manner with less effort and fewer financial resources. These transformational developments require new cheminformatics approaches to make huge chemical spaces searchable and analyzable with low resources, and with as little energy consumption as possible. Substantial progress has been made in the past years with respect to computation as well as organic synthesis. First examples of bioactive compounds resulting from the successful use of these novel technologies demonstrate their power to contribute to tomorrow's drug discovery programs. This article gives a compact overview of the state-of-the-art.

Early Career Perspectives from Large Pharma, Software, and Start-up Companies.

We are often approached by PhD students and postdocs who wonder: What are the differences between jobs for computational chemists across different industries? This Perspective aims to answer this question by comparing our personal experiences as early career scientists at a large pharmaceutical company (large pharma), a software vendor (software), and a biotech start-up (start-up) in the format of a written Q&A panel discussion. To begin, we introduce ourselves by answering questions about our backgrounds and current positions, including comparisons of our responsibilities and the culture of the companies where we work. In the next section, we focus on the beginning of our careers, discussing the skills we needed for our first industry positions and what we learned early on. Finally, we address questions about the future of our careers including potential growth, security, and what we wished we had known earlier. We conclude by comparing and contrasting our industries, including how the size and purpose of these companies have affected our experiences.

Size Matters and How You Measure It: A Gram-Negative Antibacterial Example Exceeding Typical Molecular Weight Limits.

Monobactam antibiotic 1 is active against Gram-negative bacteria even though it has a higher molecular weight (MW) than the limit of 600 Da typically applied in designing such compounds. On the basis of 2D NMR data, the compound is able to adopt a compact conformation. The dimensions, projection area, and dipole moment derived from this conformation are compatible with porin permeation, as are locations of polar groups upon superimposition to the crystal structure of ampicillin bound to E. coli OmpF porin. Minimum inhibitory concentration (MIC) shifts in a porin knock-out strain are also consistent with 1 predominately permeating through porins. In conclusion, we describe a carefully characterized case of a molecule outside default design parameters where MW does not adequately represent the 3D shape more directly related to permeability. Leveraging 3D design criteria would open up additional chemical space currently underutilized due to limitations perceived in 2D.

Facilitating unambiguous NMR assignments and enabling higher probe density through selective labeling of all methyl containing amino acids.

The deuteration of proteins and selective labeling of side chain methyl groups has greatly enhanced the molecular weight range of proteins and protein complexes which can be studied using solution NMR spectroscopy. Protocols for the selective labeling of all six methyl group containing amino acids individually are available, however to date, only a maximum of five amino acids have been labeled simultaneously. Here, we describe a new methodology for the simultaneous, selective labeling of all six methyl containing amino acids using the 115 kDa homohexameric enzyme CoaD from E. coli as a model system. The utility of the labeling protocol is demonstrated by efficiently and unambiguously assigning all methyl groups in the enzymatic active site using a single 4D (13)C-resolved HMQC-NOESY-HMQC experiment, in conjunction with a crystal structure. Furthermore, the six fold labeled protein was employed to characterize the interaction between the substrate analogue (R)-pantetheine and CoaD by chemical shift perturbations, demonstrating the benefit of the increased probe density.

[Quality control of chemical libraries]. / Contrôle qualité des chimiothèques.

The complete sequence of the human genome has been deciphered at the dawn of the new century. This historic event immediately challenged researchers with new needs both in terms of concepts and of working methods. Each scientific community considered how it could tackle these new challenges and it quickly became clear that using small chemical molecules would help discovering and characterizing the function of new proteins. The importance of the genes that the encode new proteins could thus be established in cells, organs and whole organisms. At the initiative of a handful of researchers, French chemists have organized the collection of their molecules and provided them to biologists. By doing so they killed two birds with one stone: on the one hand they created a unique opportunity to add value to their molecules by creating the first academic chemical library, and on the other hand they stimulated the launch of biologically active molecules discovery programs by biologists from the academic sector. It was necessary, however, to raise many compounds and ensure consistent quality control, which quickly became a priority for the chemical libraries to become reliable tools.

The QSPR-THESAURUS: the online platform of the CADASTER project.

The aim of the CADASTER project (CAse Studies on the Development and Application of in Silico Techniques for Environmental Hazard and Risk Assessment) was to exemplify REACH-related hazard assessments for four classes of chemical compound, namely, polybrominated diphenylethers, per and polyfluorinated compounds, (benzo)triazoles, and musks and fragrances. The QSPR-THESAURUS website (http: / /qspr-thesaurus.eu) was established as the project's online platform to upload, store, apply, and also create, models within the project. We overview the main features of the website, such as model upload, experimental design and hazard assessment to support risk assessment, and integration with other web tools, all of which are essential parts of the QSPR-THESAURUS.

Quantitative structure-property relationship modeling: a valuable support in high-throughput screening quality control.

Evaluation of important pharmacokinetic properties such as hydrophobicity by high-throughput screening (HTS) methods is a major issue in drug discovery. In this paper, we present measurements of the chromatographic hydrophobicity index (CHI) on a subset of the French chemical library Chimiothèque Nationale (CN). The data were used in quantitative structure-property relationship (QSPR) modeling in order to annotate the CN. An algorithm is proposed to detect problematic molecules with large prediction errors, called outliers. In order to find an explanation for these large discrepancies between predicted and experimental values, these compounds were reanalyzed experimentally. As the first selected outliers indeed had experimental problems, including hydrolysis or sheer absence of expected structure, we herewith propose the use of QSPR as a support tool for quality control of screening data and encourage cooperation between experimental and theoretical teams to improve results. The corrected data were used to produce a model, which is freely available on our web server at http://infochim.u-strasbg.fr/webserv/VSEngine.html .

Individual Hydrogen-Bond Strength QSPR Modelling with ISIDA Local Descriptors: a Step Towards Polyfunctional Molecules.

Here, we introduce new ISIDA fragment descriptors able to describe "local" properties related to selected atoms or molecular fragments. These descriptors have been applied for QSPR modelling of the H-bond basicity scale pKBHX , measured by the 1 : 1 complexation constant of a series of organic acceptors (H-bond bases) with 4-fluorophenol as the reference H-bond donor in CCl4 at 298 K. Unlike previous QSPR studies of H-bond complexation, the models based on these new descriptors are able to predict the H-bond basicity of different acceptor centres on the same polyfunctional molecule. QSPR models were obtained using support vector machine and ensemble multiple linear regression methods on a set of 537 organic compounds including 5 bifunctional molecules. They were validated with cross-validation procedures and with two external test sets. The best model displays good predictive performance on a large test set of 451 mono- and bifunctional molecules: a root-mean squared error RMSE=0.26 and a determination coefficient R(2) =0.91. It is implemented on our website (http://infochim.u-strasbg.fr/webserv/VSEngine.html) together with the estimation of its applicability domain and an automatic detection of potential H-bond acceptors.

ISIDA Property-Labelled Fragment Descriptors.

ISIDA Property-Labelled Fragment Descriptors (IPLF) were introduced as a general framework to numerically encode molecular structures in chemoinformatics, as counts of specific subgraphs in which atom vertices are coloured with respect to some local property/feature. Combining various colouring strategies of the molecular graph - notably pH-dependent pharmacophore and electrostatic potential-based flagging - with several fragmentation schemes, the different subtypes of IPLFs may range from classical atom pair and sequence counts, to monitoring population levels of branched fragments or feature multiplets. The pH-dependent feature flagging, pursued at the level of each significantly populated microspecies involved in the proteolytic equilibrium, may furthermore add some competitive advantage over classical descriptors, even when the chosen fragmentation scheme is one of the state-of-the-art pattern extraction procedures (feature sequence or pair counts, etc.) in chemoinformatics. The implemented fragmentation schemes support counting (1) linear feature sequences, (2) feature pairs, (3) circular feature fragments a.k.a. "augmented atoms" or (4) feature trees. Fuzzy rendering - optionally allowing nonterminal fragment atoms to be counted as wildcards, ignoring their specific colours/features - ensures for a seamless transition between the "strict" counts (sequences or circular fragments) and the "fuzzy" multiplet counts (pairs or trees). Also, bond information may be represented or ignored, thus leaving the user a vast choice in terms of the level of resolution at which chemical information should be extracted into the descriptors. Selected IPLF subsets were - tree descriptors, in particular - successfully tested in both neighbourhood behaviour and QSAR modelling challenges, with very promising results. They showed excellent results in similarity-based virtual screening for analogue protease inhibitors, and generated highly predictive octanol-water partition coefficient and hERG channel inhibition models.