Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks
Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10 23 - 10 60 ), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required changes to the prototype. In this work, we develop an algorithmic unsupervised-approach that automatically generates potential drug molecules given a prototype drug. We show that the molecules generated by the system are valid molecules and significantly different from the prototype drug. Out of the compounds generated by the system, we identified 35 FDA-approved drugs. As an example, our system generated Isoniazid - one of the main drugs for Tuberculosis. The system is currently being deployed for use in collaboration with pharmaceutical companies to further analyze the additional generated molecules.
https://www.youtube.com/watch?v=-Q_XyURjxww
メモ
ジャーナルverあり
教師無しの新薬推定,
プロトタイプ(元の薬のデータ)をVAEによりencode/decodeすることにより新薬を生成する
Key point
SMILE, Simplified Molecular-Input Line-Entry Sysytem
薬品の構造を文字列に変換する
文字列へ変換することで、ML手法等を適用できるようにしている...
一般的な文字列のように扱っても大丈夫なのが面白い, Embeddingもone-hotとかでやってるらしい
CDN,
VAE,
実験
Novelの指標において、従来手法に比べて高い値