OTAVAchemicals Innovative Therapeutic Targets Library (2989 compounds in total) is designed to accelerate the discovery of inhibitors for key proteins, including farnesyl protein transferase (FPT), HIV-1 capsid protein p24 (GAG), exportin 1 (XPO1), and β-cardiac myosin 7 (MYH7). Developed using DrugBank and ZINC databases and incorporating machine learning for virtual screening, this library offers a curated collection of molecules for advancing drug development by enhancing screening efficiency and accuracy. 

The use of PAMNet (Physics-Aware Multiplex Network) [1] technology ensures precise protein-ligand interaction predictions by leveraging both global and local molecular features through a physics-informed graph-based approach:

Exportin 1 (XPO1) Targeted Inhibitors - 707 compounds

XPO1 is overexpressed in various cancers, including lymphomas and multiple myeloma, making it a critical target for anticancer therapies like selinexor. It also plays a key role in exporting viral RNA during infections, including HIV. Targeting XPO1 inhibits these processes, disrupting cancer progression and viral replication. 

β-Cardiac Myosin 7 (MYH7) Targeted Inhibitors - 785 compounds: 

MYH7 is linked to hypertrophic cardiomyopathy and heart failure. Mutations in MYH7 impair heart function. Inhibitors targeting MYH7 can address these dysfunctions, providing therapeutic options for cardiovascular diseases. 

Farnesyl Protein Transferase (FPT) Targeted Inhibitors - 1773 compounds: 

FPT regulates cell proliferation and survival, making it a prime target in cancers like breast and lung cancer. Inhibiting FPT also has potential benefits for cardiovascular conditions linked to atherosclerosis. 

HIV-1 Capsid Protein p24 (GAG) Targeted Inhibitors - 1100 compounds: 

p24 is essential for HIV replication and serves as a diagnostic marker. Inhibiting p24 reduces viral replication and enhances the effectiveness of antiretroviral therapies.

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1. Zhang, S., Liu, Y., & Xie, L. (2023). A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems. Scientific Reports, 13(1), 19171. Nature Publishing Group UK London