Applications of single-cell RNA sequencing in drug discovery and development.

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action.

Applications of machine learning in drug discovery and development.

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery.

Virtual screening on an α-helix to β-strand switchable region of the FGFR2 extracellular domain revealed positive and negative modulators.

The secondary structure of some protein segments may vary between α-helix and β-strand. To predict these switchable segments, we have developed an algorithm, Switch-P, based solely on the protein sequence. This algorithm was used on the extracellular parts of FGF receptors.

Targeting the TLR co-receptor CD14 with TLR2-derived peptides modulates immune responses to pathogens.

Dysregulation of Toll-like receptor (TLR) responses to pathogens can lead to pathological inflammation or to immune hyporesponsiveness and susceptibility to infections, and may affect adaptive immune responses. TLRs are therefore attractive therapeutic targets. We assessed the potential of the TLR co-receptor CD14 as a target for therapeutics by investigating the magnitude of its influence on TLR responses.

A Strategy Combining Differential Low-Throughput Screening and Virtual Screening (DLS-VS) Accelerating the Discovery of new Modulators for the Orphan GPR34 Receptor.

The DLS-VS strategy was developed as an integrated method for identifying chemical modulators for orphan GPCRs. It combines differential low-throughput screening (DLS) and virtual screening (VS). The two cascaded techniques offer complementary advantages and allow the experimental testing of a minimal number of compounds.

Differential Virtual Screening (DVS) with Active and Inactive Molecular Models for Finding and Profiling GPCR Modulators: Case of the CCK1 Receptor.

We discovered a constitutively activating mutation (CAM) V308E for the neurotensin NT1 receptor. Molecular dynamics (MD) performed for the CAM NT1-V308E exhibiting a high spontaneous activity, and for the wild-type NT1 without basal activity, show dramatic conformational changes for the CAM. To test if the two MD models could be valuable active and inactive templates for building molecular models for other class-A GPCR, supposed active and inactive models were built by homology for the cholecystokinin CCK1 receptor.