AI Molecule Design Using Generative-Predictive Reinforcement Learning
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Solution Overview
Problem
The high attrition rates and inefficiencies in drug development due to complex chemical spaces and reliance on human intuition lead to costly failures, making it challenging to discover drugs with desired properties efficiently.
Innovation Solution
A deep reinforcement learning (RL) based method using generative and predictive neural networks to automate the design of molecules, where the generative model acts as an agent and the predictive model as a critic, optimizing molecular properties through a reward system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive searching of chemical space is performed, then all possible drug candidates can be evaluated, but the computational resources and time required become prohibitively large
Solution Approach 1:
The patent applies preliminary action by pre-training generative models on existing molecular data to learn chemical space distributions before actual drug candidate generation. This pre-learning phase enables the model to efficiently explore relevant regions of chemical space without exhaustive searching, significantly reducing computational time while maintaining reliability in candidate evaluation
Solution Approach 2:
The patent uses copying by generating virtual molecular copies through the generative model instead of physically synthesizing and testing every possible candidate. The model learns from training data and generates synthetic molecular structures with predicted properties, avoiding the need to exhaustively evaluate all possible chemical compounds in reality
2Productivity
If local optimization is used to navigate chemical space, then the search process becomes computationally feasible, but the solution may converge on local optima rather than global optima
Solution Approach 1:
The patent applies dynamics by using a recurrent neural network with dynamic memory mechanisms that can adapt and evolve during the generation process. The model dynamically adjusts its exploration strategy, balancing between exploiting known good regions and exploring new areas of chemical space, thereby avoiding premature convergence to local optima while maintaining computational efficiency
Solution Approach 2:
The patent implements feedback through reinforcement learning where the model receives feedback signals based on the quality of generated molecules. This feedback mechanism allows the model to learn from its own outputs and adjust its generation strategy, improving both the optimality of solutions and the efficiency of the search process over time
3Adaptability or versatility
If human experts generate design hypotheses based on intuition and expertise, then domain knowledge is leveraged, but the process is biased towards preferred chemistry and model interpretation
Solution Approach 1:
The patent applies self-service by enabling the system to automatically generate and optimize molecular designs without human intervention. The generative model learns from training data and autonomously explores chemical space, eliminating human biases towards preferred chemistry while still leveraging domain knowledge embedded in the training data. The system serves itself by continuously improving through reinforcement learning feedback
Data Source
AI summary
The subject matter described herein includes computational methods, systems and non-transitory computer readable media for de-novo drug discovery, which is based on deep learning and reinforcement learning techniques. The subject matter described herein allows generating chemical compounds with desired properties. Two deep neural networks-generative and predictive, represent the general workflow. The process of training consists of two stages. During the first stage, both models are trained separately with supervised learning algorithms, and during the second stage, models are trained jointly with reinforcement learning approach. In this study, we conduct a computational experiment, which demonstrates the efficiency of proposed strategy to maximize, minimize or impose a desired range to a property. We also thoroughly evaluate our models with quantitative approaches and provide visualization and interpretation of internal representation vectors for both predictive and generative models.