We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

<div> <div> <div> <p>We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behavior at a proteomic level by constructing and analyzing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls (p-value < 10-12). Also, we analyzed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders. </p> </div> </div> </div>

Phosphothreonine (pThr)-embedded peptide catalysts are found to mediate the reductive amination of 3-amidocyclohexanones with divergent selectivity. The choice of peptide sequence can be used to alter the diastereoselectivity to favor either the cis-product or trans-product, which are obtained in up to 93:7 er. NMR studies and DFT calculations are reported and indicate that both pathways rely on secondary interactions between substrate and catalyst to achieve selectivity. Furthermore, catalysts appear to accomplish a parallel kinetic resolution of the substrates. The facility for phosphopeptides to tune reactivity and access multiple products in reductive aminations may translate to the diversification of complex substrates, such as natural products, at numerous reactive sites.

<div> <div> <div> <p>We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behavior at a proteomic level by constructing and analyzing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls (p-value < 10-12). Also, we analyzed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders. </p> </div> </div> </div>

To mimic the levels of spatiotemporal control that exist in nature, tools for chemically induced dimerization (CID) are employed to manipulate protein‐protein interactions. Although linker composition is known to influence speed and efficiency of heterobifunctional compounds, modeling or in vitro experiments are often insufficient to predict optimal linker structure. This can be attributed to the complexity of ternary complex formation and the overlapping factors that impact the effective concentration of probe within the cell, such as efflux and passive permeability. Herein, we synthesize a library of modular chemical tools with varying linker structures and perform quantitative microscopy in live cells to visualize dimerization in real‐time. We use our optimized probe to demonstrate our ability to recruit a protein of interest (POI) to the mitochondria, cell membrane, and nucleus. Finally, we induce and monitor local and global phase separation. We highlight the importance of quantitative approaches to linker optimization for dynamic systems and introduce new, synthetically accessible tools for the rapid control of protein localization.