Fragment Shuffling: An Automated Workflow for Three-Dimensional Fragment-Based Ligand Design

Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search

Fragment merging is a promising approach to progressing fragments directly to on-scale potency: each designed compound incorporates the structural motifs of overlapping fragments in a way that ensures compounds recapitulate multiple high-quality interactions. Searching commercial catalogues provides one useful way to quickly and cheaply identify such merges and circumvents the challenge of synthetic accessibility, provided they can be readily identified. Here, we demonstrate that the Fragment Network, a graph database that provides a novel way to explore the chemical space surrounding fragment hits, is well-suited to this challenge. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four crystallographic screening campaigns and contrast the results with a traditional fingerprint-based similarity search. The two approaches identify complementary sets of merges that recapitulate the observed fragment-protein interactions but lie in different regions of chemical space. We further show our methodology is an effective route to achieving on-scale potency by retrospective analyses for two different targets; in analyses of public COVID Moonshot and Mycobacterium tuberculosis EthR inhibitors, potential inhibitors with micromolar IC₅₀ values were identified. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search.

MolHyb: A Web Server for Structure-Based Drug Design by Molecular Hybridization

Molecular hybridization is a widely used ligand design method in drug discovery. In this study, we present MolHyb, a web server for structure-based ligand design by molecular hybridization. The input of MolHyb is a protein file and a seed compound file. MolHyb tries to generate novel ligands through hybridizing the seed compound with helper compounds that bind to the same protein target or similar proteins. To facilitate the job of getting helper compounds, we compiled a modeled protein-ligand structure database as an extension to crystal structures in the PDB database by placing the bioactive compounds in ChEMBL into their corresponding 3D protein binding pocket properly. MolHyb works by searching for helper compounds from the protein-ligand structure database and migrating chemical moieties from helper compounds to the seed compound efficiently. Hybridization is performed at both cyclic and acyclic bonds. The users can also input their own helper compounds to MolHyb. We hope that MolHyb will be a useful tool for rational drug design. MolHyb is freely available at http://molhyb.xundrug.cn/.

De novo drug design

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Current strategies to target p53 in cancer

Tumor suppressor p53 is a transcription factor that guards the genome stability and normal cell growth. Stresses like DNA damage, oncogenic assault will turn on p53 function which leads to cell cycle arrest for DNA repair, senescence for permanent growth arrest or apoptosis for programmed cell death. At the late stage of cancer progression, p53 is hijacked in all forms of tumors either trapped in the negative regulator such as MDM2/viral proteins or directly mutated/deleted. Re-introduction of a functional p53 alone has been proven to induce tumor regression robustly. Also, an active p53 pathway is essential for effective chemo- or radio-therapy. The emerging cyclotherapy in which p53 acts as a chemoprotectant of normal tissues further expands the utility of p53 activators. Functionally, it is unquestionable that drugging p53 will render tumor-specific intervention. One direct method is to deliver the functional wild-type (wt) p53 to tumors via gene therapy. The small molecule strategies consist of activation of p53 family member such as p73, manipulating p53 posttranslational modulators to increase wt p53 protein levels, protein-protein interaction inhibitors to free wt p53 from MDM2 or viral protein, and restoring p53 function to mutant p53 by direct modulation of its conformation. Although most of the current pre-clinical leads are in microM range and need further optimization, the success in proving that small molecules can reactivate p53 marks the beginning of the clinical development of p53-based cancer therapy.