githubXin123/ReinventCommunity

ReinventCommunity (jupyter notebook tutorials for REINVENT 3.1)

This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.1. At the moment, the following notebooks are supported:

• Complete_Use-Case_DRD2_Demo: a full-fledged use case using public data on DRD2, including use of predictive models and elucidating general considerations
• Create_Model_Demo: explanation on how to initialize a new model (prior / agent) for REINVENT which can be trained in a transfer learning setup
• Data_Preparation: tutorial on how to prepare (clean, filter and standardize) data from a source such as ChEMBL to be used for training
• Model_Building_Demo: shows how to train a predictive (QSAR) model to be used with REINVENT based on the public DRD2 dataset (classification problem)
• Reinforcement_Learning_Demo: example reinforcement learning run with a selection of scoring function components to generate novel compounds with ever higher scores iteratively
• Reinforcement_Learning_Demo_Selectivity: example illustrating the use of the relatively complicated selectivity_component to optimize potency against a target while simultaneously pushing for a low potency against one or more off-targets
• Reinforcement_Learning_Demo_Tanimoto: very simple (only 1, easy-to-understand component) transfer learning example
• Reinforcement_Learning_Exploitation_Demo: illustrates the exploitation scenario, where one is after solutions from a subspace in chemical space already well defined
• Reinforcement_Learning_Exploration_Demo: illustrates the exploration scenario, where the aim is to generate a varied set of solutions to a less stringently defined problem
• Sampling_Demo: once an agent has been trained and is producing interesting results, it can be used to generate more compounds without actually changing it further - this is facilitated by the sampling mode
• Score_Transformations: as many components produce scores on an arbitrary scale, but REINVENT needs to receive it normalized to be a number between 0 and 1 (with values close to 1 meaning "good"), score transformations have been implemented and can be used as shown in this tutorial
• Scoring_Demo: in case a set of existing compound definitions (for example prior to starting a project) should be scored with a scoring function definition, the scoring mode can be used
• Transfer_Learning_Demo: this tutorial illustrates the transfer learning mode, which usually is used to "pre-train" an agent before reinforcement learning in case no adequate naive prior is available or to focus an already existing agent further
• Transfer_Learning_Demo_Teachers_Forcing: same as Transfer_Learning_Demo above, with explanation of teachers forcing
• Lib-INVENT_RL1_QSAR: Lib-INVENT example reinforcement learning run using a QSAR model
• Lib-INVENT_RL2_QSAR_RF: Lib-INVENT example reinforcement learning run using a random forest (RF) QSAR model
• Lib_INVENT_RL3_ROCS_RF: Lib-INVENT example reinforcement learning using OpenEye's ROCS 3D similarity (requires an OpenEye license)
• Automated_Curriculum_Learning_demo: illustrates the automated curriculum learning running model. The example demonstrates how to set-up a curriculum to guide the REINVENT agent to sample a target molecular scaffold. This scenario represents a complex objective as the target scaffold is not present in the training set for the prior model