| CPC G16B 5/00 (2019.02) [G16B 15/30 (2019.02); G16B 40/20 (2019.02)] | 27 Claims | 

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               1. A small molecule drug discovery method comprising the steps of: 
            (a) generating data representing a transition state occurring within a specific enzyme and a substrate reaction, using quantum mechanics and molecular dynamics based simulation of the specific enzyme and substrate reaction, running on a computer-implemented simulation engine, in which the data representing the transition state further includes a description of the quantum chemical properties of the specific enzyme and the substrate in the transition state; 
                (b) storing the data representing the transition state (‘the quantum pharmacophore’) in a memory; 
                (c) passing the quantum pharmacophore into a computer-implemented machine learning engine configured to generate data defining transition state analogues, in which transition state analogues are small molecules that enable the specific enzyme to enter its transition state; and 
                in which the computer-implemented machine learning engine is a generative ML system configured to optimise a cost function; and 
                in which the cost function reflects a similarity of each generated transition state analogue to the quantum pharmacophore, in which the similarity is assessed in terms of one or more of the following molecular properties: distribution of charges or Van der Waals dispersion or tensor network like description of the transition state or properties assessed via wetlab assays and feedback loops. 
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