Privacy-preserving search for chemical compound databases.

Area of Science:

• Computational chemistry
• Cryptography
• Bioinformatics

Background:

• In-silico drug screening relies on searching chemical databases.
• Privacy concerns prevent secure data sharing between query holders and database holders.
• This limits access to valuable chemical data for drug discovery.

Purpose of the Study:

• To develop a novel cryptographic protocol for privacy-preserving database searching.
• To enable secure in-silico drug screening without compromising user or database privacy.
• To overcome the limitations of current data sharing practices in drug discovery.

Main Methods:

• Developed a protocol using an additive-homomorphic cryptosystem.
• The protocol allows computations on encrypted data efficiently.
• Avoided computationally expensive general-purpose multi-party computation techniques.

Main Results:

• Achieved privacy preservation for both query and database holders.
• Demonstrated significant computational and communication efficiency compared to existing methods.
• Successfully searched the ChEMBL database (1,200,000+ compounds) rapidly.

Conclusions:

• A novel privacy-preserving protocol for chemical database searching has been proposed.
• The method is scalable for large databases and computationally efficient.
• This facilitates drug discovery by enabling the use of sensitive but valuable data.