Discovery of TIGIT inhibitors based on DEL and machine learning

Inhibitors of Immune Checkpoints: Small Molecule- and Peptide-Based Approaches

The revolutionary progress in cancer immunotherapy, particularly the advent of immune checkpoint inhibitors, marks a significant milestone in the fight against malignancies. However, the majority of clinically employed immune checkpoint inhibitors are monoclonal antibodies (mAbs) with several limitations, such as poor oral bioavailability and immune-related adverse effects (irAEs). Another major limitation is the restriction of the efficacy of mAbs to a subset of cancer patients, which triggered extensive research efforts to identify alternative approaches in targeting immune checkpoints aiming to overcome the restricted efficacy of mAbs. This comprehensive review aims to explore the cutting-edge developments in targeting immune checkpoints, focusing on both small molecule- and peptide-based approaches. By delving into drug discovery platforms, we provide insights into the diverse strategies employed to identify and optimize small molecules and peptides as inhibitors of immune checkpoints. In addition, we discuss recent advances in nanomaterials as drug carriers, providing a basis for the development of small molecule- and peptide-based platforms for cancer immunotherapy. Ongoing research focused on the discovery of small molecules and peptide-inspired agents targeting immune checkpoints paves the way for developing orally bioavailable agents as the next-generation cancer immunotherapies.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.

Small-molecule discovery through DNA-encoded libraries

The development of bioactive small molecules as probes or drug candidates requires discovery platforms that enable access to chemical diversity and can quickly reveal new ligands for a target of interest. Within the past 15 years, DNA-encoded library (DEL) technology has matured into a widely used platform for small-molecule discovery, yielding a wide variety of bioactive ligands for many therapeutically relevant targets. DELs offer many advantages compared with traditional screening methods, including efficiency of screening, easily multiplexed targets and library selections, minimized resources needed to evaluate an entire DEL and large library sizes. This Review provides accounts of recently described small molecules discovered from DELs, including their initial identification, optimization and validation of biological properties including suitability for clinical applications.

3CLpro inhibitors: DEL-based molecular generation

Molecular generation (MG) via machine learning (ML) has speeded drug structural optimization, especially for targets with a large amount of reported bioactivity data. However, molecular generation for structural optimization is often powerless for new targets. DNA-encoded library (DEL) can generate systematic, target-specific activity data, including novel targets with few or unknown activity data. Therefore, this study aims to overcome the limitation of molecular generation in the structural optimization for the new target. Firstly, we generated molecules using the structure-affinity data (2.96 million samples) for 3C-like protease (3CLpro) from our own-built DEL platform to get rid of using public databases (e.g., CHEMBL and ZINC). Subsequently, to analyze the effect of transfer learning on the positive rate of the molecule generation model, molecular docking and affinity model based on DEL data were applied to explore the enhanced impact of transfer learning on molecule generation. In addition, the generated molecules are subjected to multiple filtering, including physicochemical properties, drug-like properties, and pharmacophore evaluation, molecular docking to determine the molecules for further study and verified by molecular dynamics simulation.