Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

Fragment based drug design has emerged as an effective alternative to high throughput screening for the identification of lead compounds in drug discovery in the past fifteen years. Fragment based screening and optimization methods have achieved credible success in many drug discovery projects with one approved drug and many more compounds in clinical trials. The fragment based drug design starts with the identification of fragments or low molecular weight compounds that generally bind with weak affinity to the target of interest. The fragments that form high quality interactions are then optimized to lead compounds with high affinity and selectivity. The weak affinity of fragments for their target requires the use of biophysical techniques such as nuclear magnetic resonance, X-ray crystallography or surface plasmon resonance to identify hits. These techniques are very sensitive and some of them provide detailed protein fragment interaction information that is important for fragment to lead optimization. Despite the huge advances in technology in the past years, experimental methods of fragment screening suffer several challenges such as low throughput, high cost of instruments and experiments, high protein and fragment concentration requirements. To address challenges posed by experimental screening approaches, computational methods were developed that play an important role in fragment library design, fragment screening and optimization of initial fragment hits. The computational approaches of fragment screening and optimization are most useful when they are used in combination with experimental approaches. The use of virtual fragment based screening in combination with experimental methods has fostered the application of fragment based drug design to important biological targets including protein-protein interactions and membrane proteins such as GPCRs. This review provides an overview of experimental and computational screening approaches used in fragment based drug discovery with an emphasis on recent successes achieved in discovering potent lead molecules using these approaches.

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Conjugation of small ubiquitin-like modifier (SUMO) to protein (SUMOylation) regulates multiple biological systems by changing the functions and fates of a large number of proteins. Consequently, abnormalities in SUMOylation have been linked to multiple diseases, including breast cancer. Using an in situ cell-based screening system, we have identified spectomycin B1 and related natural products as novel SUMOylation inhibitors. Unlike known SUMOylation inhibitors such as ginkgolic acid, spectomycin B1 directly binds to E2 (Ubc9) and selectively blocks the formation of the E2-SUMO intermediate; that is, Ubc9 is the direct target of spectomycin B1. Importantly, either spectomycin B1 treatment or Ubc9 knockdown inhibited estrogen-dependent proliferation of MCF7 human breast-cancer cells. Our findings suggest that Ubc9 inhibitors such as spectomycin B1 have potential as therapeutic agents against hormone-dependent breast cancers.

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