Cyclica Inc

Publications

Peer-reviewed publications are denoted with an asterisk (*).

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December 2, 2021

Novel structural-related analogs of PFI-3 (SRAPs) that target the BRG1 catalytic subunit of the SWI/SNF complex increase the activity of temozolomide in glioblastoma cells*

Yali He, Chuanhe Yang, Yinan Wang, Joshua R. Sacher, Michelle M. Sims, Lawrence M. Pfeffer, and Duane D. Miller.

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December 2, 2021

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease*

Michael G. Sugiyama, Haotian Cui, Dar’ya S. Redka, Mehran Karimzadeh, Edurne Rujas, Hassaan Maan, Sikander Hayat, Kyle Cheung, Rahul Misra, Joseph B. McPhee, Russell D. Viirre, Andrew Haller, Roberto J. Botelho, Raffi Karshafian, Sarah A. Sabatinos, Gregory D. Fairn, Seyed Ali Madani Tonekaboni, Andreas Windemuth, Jean-Philippe Julien, Vijay Shahani, Stephen S. MacKinnon, Bo Wang, and Costin N. Antonescu.

November 23, 2021

Proteome-Scale Drug-Target Interaction Predictions: Approaches and Applications*

Stephen Scott MacKinnon, S. A. Madani Tonekaboni, and Andreas Windemuth.

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June 28, 2021

Genetic variability of the SARS-CoV-2 pocketome*

Setayesh Yazdani, Nicola De Maio, Yining Ding, Vijay Shahani, Nick Goldman, and Matthieu Schapira.

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April 13, 2021

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease

Michael G. Sugiyama, Haotian Cui, Dar’ya S. Redka, Mehran Karimzadeh, Edurne Rujas, Hassaan Maan, Sikander Hayat, Kyle Cheung, … (+15 more).

August 17, 2020

Assessing methods and obstacles in chemical space exploration*

Shawn Reeves, Benjamin DiFrancesco, Vijay Shahani, Stephen MacKinnon, Andreas Windemuth, and Andrew Brereton.

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June 3, 2020

Learning across label confidence distributions using Filtered Transfer Learning*

Seyed Ali Madani Tonekaboni, Andrew E. Brereton, Zhaleh Safikhani, Andreas Windemuth, Benjamin Haibe-Kains, and Stephen MacKinnon.

May 19, 2020

Predicting drug properties with parameter-free machine learning: Pareto-Optimal Embedded Modeling (POEM)*

Andrew E. Brereton, Stephen MacKinnon, Zhaleh Safikhani, Shawn Reeves, Sana Alwash, Vijay Shahani, and Andreas Windemuth.

April 6, 2020

PolypharmDB quickly identifies repurposed drug candidates for COVID-19

Dar’ya S. Redka, Stephen S. MacKinnon, Melissa Landon, AndreasWindemuth, Naheed Kurji, and Vijay Shahani.

March 23, 2018

Structural pharmacogenomics identifies putative binding sites*

Steven V. Molinski, Vijay M. Shahani, Adithya S. Subramanian, Stephen S. MacKinnon, Geoffrey Woollard, Marcon Laforet, Onofrio Laselva, Leonard D. Morayniss, Christine E. Bear, and Andreas Windemuth.

November 14, 2017

Giving drugs a second chance: Overcoming regulatory and financial hurdles in repurposing approved drugs as cancer therapeutics*

J. Javier Hernandez, Michael Pryszlak, Lindsay Smith, Connor Yanchus, Naheed Kurji, Vijay M. Shahani, and Steven V. Molinski.

September 21, 2017

The antiretroviral agent nelfinavir mesylate: A potential therapy for systemic sclerosis*

Cecilia G. Sanchez, Steven V. Molinski, Rafael Gongora, Meredith Sosulski, Taylor Fuselier, Stephen S. MacKinnon, Debasis Mondal, and Joseph A. Lasky.

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August 23, 2017

Structural coverage of the proteome for pharmaceutical applications*

Joseph C. Somody, Stephen S. MacKinnon, and Andreas Windemuth.

December 17, 2016

Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474*

Steven V. Molinski, Vijay M. Shahani, Stephen S. MacKinnon, Leonard D. Morayniss, Marc Laforet, Geoffrey Woollard, Naheed Kurji, Cecilia G. Sanchez, Shoshana J. Wodak, and Andreas Windemuth.

Case Studies

April 28, 2022

Designing chemical probes for DCAF1 using MatchMaker™

Cyclica partnered with the Structural Genomics Consortium (SGC) to design a chemical probe for DCAF1, a low data WDR protein.

July 28, 2021

Cyclica’s Ligand Design platform identifies active molecules for repurposing which can be used to treat Parkinson’s

Cyclica partners with Kalia labs to accelerate the discovery of critical medicines for patients suffering from neurodegenerative diseases.

June 29, 2021

Comparison of MatchMaker to DeepConv-DTI reveals superior performance and compute efficiency for predicting drug-target interactions

Cyclica’s MatchMaker deep learning engine excels at dataless protein targets.

June 25, 2021

MatchMaker Validation on newly published protein-ligand interaction dataset

Proteome screening, powered by Cyclica’s MatchMaker deep learning engine, predicts primary protein targets of newly published ligands.

April 1, 2021

MatchMaker Proteome Screening identified more drug-target interactions than CETSA® MS, KiNativ™ or affinity purification

MatchMaker compared with experimental approaches for target deconvolution.

Predicting hERG channel liabilities

POEM, Cyclica’s supervised machine learning algorithm, predicts hERG activity for small molecules.

Rapid discovery of a sirtuin 2 inhibitor using Ligand Design

Ligand Design leverages polypharmacology to find both specific and multi-targeted hits through the exploration of a commercial library.

Proteome Screening with MatchMaker achieves performance comparable to experimental techniques

MatchMaker-powered Proteome Screening identifies more known binders of methotrexate, dasatinib, and panobinostat compared to thermal proteome profiling (TPP).

Evaluating Cyclica’s AI-based ADMET Prediction tool

The ADMET Prediction tool consistently outperforms traditional QSAR approaches for predicting pharmacokinetic and toxicity properties.

MatchMaker: A leap forward in proteome screening beyond molecular docking

MatchMaker combines molecular biophysics and deep learning to predict binding of new drug molecules to all proteins with high speed, accuracy, and generalizability, moving beyond the reliance of molecular docking.

The Ligand Express™ platform guides drug repurposing study

Top drug repositioning candidates selected by anticipating the biological consequences of predicted ligand-protein interactions.