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Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machinereadable format, as this is central to computational chemistry. Four classes of representations are introduced: string, connection table, feature-based, and computer-learned representations. Three of the most significant representations are simplified molecular-input line-entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked.Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible.
Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machinereadable format, as this is central to computational chemistry. Four classes of representations are introduced: string, connection table, feature-based, and computer-learned representations. Three of the most significant representations are simplified molecular-input line-entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked.Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible.
The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty. Graphical abstract SpaceGrow descriptor comparison for an example cut in the molecule of interest. Scoring scheme is implied for one fragment of this cut.
Histone deacetylases (HDACs) are overexpressed in cancer, and their inhibition shows promising results in cancer therapy. In particular, selective class I HDAC inhibitors such as entinostat are proposed to be more beneficial in breast cancer treatment. Computational drug design is an inevitable part of today's drug discovery projects because of its unequivocal role in saving time and cost. Using three HDAC inhibitors trichostatin, vorinostat, and entinostat as template structures and a diverse fragment library, all synthetically accessible compounds thereof (∼3200) were generated virtually and filtered based on similarity against the templates and PAINS removal. The 298 selected structures were docked into the active site of HDAC I and ranked using a calculated binding affinity. Top-ranking structures were inspected manually, and, considering the ease of synthesis and drug-likeness, two new structures (3a and 3b) were proposed for synthesis and biological evaluation. The synthesized compounds were purified to a degree of more than 95% and structurally verified using various methods. The designed compounds 3a and 3b showed 65−80 and 5% inhibition on HDAC 1, 2, and 3 isoforms at a concentration of 10 μM, respectively. The novel compound 3a may be used as a lead structure for designing new HDAC inhibitors.
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