Hebrew University algorithm dramatically improves drug discovery methods

An algorithm that cuts through the immense number of possible solutions to shorten drug discovery times from years to months has been developed by Hebrew University of Jerusalem scientists and is expected to lead to dramatic improvement in drug discovery methods. For his work on the algorithm, Prof. Amiram Goldblum of the Institute for Drug Research of the Hebrew University School of Pharmacy was recently awarded a 2017 Kaye Innovation Award. In recent years, antibiotics and various molecules have been discovered that block immune system overreaction and inhibit the growth of cancer cells by removing excess iron and increasing fat digestion. The discoveries were made with an algorithm – a unique computerized approach to solving particularly complex problems through the use of specifically designed processes and calculations. Over the past five years, an Iterative Stochastic Elimination algorithm has been developed in Goldblum's lab and applied to the discovery of potential drugs. First tested to solve problems in the structure and function of proteins, the algorithm has since been used to reduce drug discovery times – from years to months and even to weeks. Goldblum's solution is different from other algorithms called “heuristics,” which are based on deriving solutions using logic and intuition and suggests better solutions. In this instance, the algorithm produces a model for the activity of small molecules on one or more proteins known to cause the disease. A model is a set of filters of physico-chemical properties that distinguish between active and non-active molecules or between more and less active ones. Millions of molecules can then be screened by the model, which enables the scoring of each molecule by a number that reflects its ability to pass through the filters based on its own physico-chemical properties. A model of this type is usually built in a few hours and is capable of screening millions of molecules in less than a day. So within a few days or more, it is possible to make initial predictions about the candidate molecules for a specific activity to combat a disease. Most of those candidates have never been known before to have any biological activity. Goldblum's algorithm has already solved many problems related to understanding various biological systems such as protein flexibility, proteins- small molecules interactions and more. His predictions have been tested by labs and drug companies in the US, Germany, Japan and Israel. The process or set of rules to be followed in calculations or other problem-solving operations can be applied to an immense number of other types of problems, the university said on Thursday. These include problems in which the number of possible outcomes are 10 to the power of 100 (10100) and more, such as problems of land transport, aviation, communications and biological systems. In the field of transportation, this could involve finding alternative ways to get from one point to another using traffic data on each of the alternative roads leading between the two points. In aviation, an optimal arrangement of landings and takeoffs at busy airports. In telecommunications, finding the least expensive routes within a complex array of communication cables. In biology, a model that is constructed on the basis of a few dozen or hundreds of molecules serves to screen millions of molecules and to discover new drug candidates. These are then sent to experimental labs to be developed further, and in some cases have been crucial in furthering the development of treatment for Alzheimer's disease and different forms of cancer. The Kaye Innovation Award was established in 1994 by prominent UK pharmaceutical industrialist Isaac Kay to encourage Hebrew University faculty, staff and students develop innovative ideas with good commercial potential to benefit the university and society.

Hebrew University algorithm dramatically improves drug discovery methods

Goldblum’s algorithm has already solved many problems related to understanding various biological systems such as protein flexibility, proteins- small molecules interactions and more.

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