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.

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.

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

Monobactam antibiotic 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 OmpF porin.

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.

[Quality control of chemical libraries].

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.

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.

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.

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.

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.