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Fragment‐based drug discovery (FBDD) is now established as a complementary approach to high‐throughput screening (HTS). Contrary to HTS, where large libraries of drug‐like molecules are screened, FBDD screens involve smaller and less complex molecules which, despite a low affinity to protein targets, display more ‘atom‐efficient’ binding interactions than larger molecules. Fragment hits can, therefore, serve as a more efficient start point for subsequent optimisation, particularly for hard‐to‐drug targets. Since the number of possible molecules increases exponentially with molecular size, small fragment libraries allow for a proportionately greater coverage of their respective ‘chemical space’ compared with larger HTS libraries comprising larger molecules. However, good library design is essential to ensure optimal chemical and pharmacophore diversity, molecular complexity, and physicochemical characteristics. In this review, we describe our views on fragment library design, and on what constitutes a good fragment from a medicinal and computational chemistry perspective. We highlight emerging chemical and computational technologies in FBDD and discuss strategies for optimising fragment hits. The impact of novel FBDD approaches is already being felt, with the recent approval of the covalent KRASG12C inhibitor sotorasib highlighting the utility of FBDD against targets that were long considered undruggable. Fragment‐based drug discovery (FBDD) uses collections (libraries) of smaller, less complex molecules (fragments) than drug‐like molecules used in high‐throughput screening. Herein, we highlight the advantages of FBDD, which has already resulted in several marketed drugs. Finding quality FBDD hits depends on good library design. We describe our views on chemical and computational aspects of library design and discuss emerging technologies.

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Nuclear magnetic resonance (NMR) spectroscopy has evolved into a powerful tool for fragment-based drug discovery over the last two decades. While NMR has been traditionally used to elucidate the three-dimensional structures and dynamics of biomacromolecules and their interactions, it can also be a very valuable tool for the reliable identification of small molecules that bind to proteins and for hit-to-lead optimization. Here, we describe the use of NMR spectroscopy as a method for fragment-based drug discovery and how to most effectively utilize this approach for discovering novel therapeutics based on our experience.

The 3 human RAS genes, KRAS, NRAS, and HRAS, encode 4 different RAS proteins which belong to the protein family of small GTPases that function as binary molecular switches involved in cell signaling. Activating mutations in RAS are among the most common oncogenic drivers in human cancers, with KRAS being the most frequently mutated oncogene. Although KRAS is an excellent drug discovery target for many cancers, and despite decades of research, no therapeutic agent directly targeting RAS has been clinically approved. Using structure-based drug design, we have discovered BI-2852 (1), a KRAS inhibitor that binds with nanomolar affinity to a pocket, thus far perceived to be “undruggable,” between switch I and II on RAS; 1 is mechanistically distinct from covalent KRASG12C inhibitors because it binds to a different pocket present in both the active and inactive forms of KRAS. In doing so, it blocks all GEF, GAP, and effector interactions with KRAS, leading to inhibition of downstream signaling and an antiproliferative effect in the low micromolar range in KRAS mutant cells. These findings clearly demonstrate that this so-called switch I/II pocket is indeed druggable and provide the scientific community with a chemical probe that simultaneously targets the active and inactive forms of KRAS.

In this review, a general introduction to fragment-based drug design and the underlying concepts is given. General considerations and methodologies ranging from library selection/construction over biophysical screening and evaluation methods to in-depth hit qualification and subsequent optimization strategies are discussed. These principles can be generally applied to most classes of drug targets. The examples given for fragment growing, merging, and linking strategies at the end of the review are set in the fields of enzyme-inhibitor design and macromolecule–macromolecule interaction inhibition. Building upon the foundation of fragment-based drug discovery (FBDD) and its methodologies, we also highlight a few new trends in FBDD.

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small-molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic, there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high-resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 153 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated conformational changes within the active site, and key inhibitor motifs that will template future drug development against Mac1.

Fragment-based drug discovery (FBDD) using NMR has become a central approach over the last twenty years for development of small molecule inhibitors against biological macromolecules, to control a variety of cellular processes. Yet, several considerations should be taken into account for obtaining a therapeutically relevant agent. In this review, we aim to list the considerations that make NMR fragment screening a successful process for yielding potent inhibitors. Factors that may govern the competence of NMR in fragment based drug discovery are discussed, as well as later steps that involve optimization of hits obtained by NMR-FBDD.

RNAs have important functions that are dictated by their structure. Indeed, small molecules that interact with RNA structures can perturb function, serving as chemical probes and lead medicines. Here we describe the development of a fragment-based approach to discover and optimize bioactive small molecules targeting RNA. We extended the target validation method chemical cross-linking and isolation by pull-down (Chem-CLIP) to identify and map the binding sites of low molecular weight fragments that engage RNA or Chem-CLIP fragment mapping (Chem-CLIP-Frag-Map). Using Chem-CLIP-Frag-Map, we identified several fragments that bind the precursor to oncogenic microRNA-21 (pre-miR-21). Assembly of these fragments provided a specific bioactive compound with improved potency that inhibits pre-miR-21 processing, reducing mature miR-21 levels. The compound exerted selective effects on the transcriptome and selectively mitigated a miR-21–associated invasive phenotype in triple-negative breast cancer cells. The Chem-CLIP-Frag-Map approach should prove general to expedite the identification and optimization of small molecules that bind RNA targets.

YEATS (YAF9, ENL, AF9, TAF14, SAS5) family proteins recognize acylated histones and in turn regulate chromatin structure, gene transcription, and stress signaling. The chromosomal translocations of ENL and mixed lineage leukemia are considered oncogenic drivers in acute myeloid leukemia and acute lymphoid leukemia. However, known ENL YEATS domain inhibitors have failed to suppress the proliferation of 60 tested cancer cell lines. Herein, we identified four hits from the NMR fragment-based screening against the AF9 YEATS domain. Ten inhibitors of new chemotypes were then designed and synthesized guided by two complex structures and affinity assays. The complex structures revealed that these inhibitors formed an extra hydrogen bond to AF9, with respect to known ENL inhibitors. Furthermore, these inhibitors demonstrated antiproliferation activities in AF9-sensitive HGC-27 cells, which recapitulated the phenotype of the CRISPR studies against AF9. Our work will provide the basis for further structured-based optimization and reignite the campaign for potent AF9 YEATS inhibitors as a precise treatment for AF9-sensitive cancers.

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their “undruggable” binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.