Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep imitation learning for molecular inverse problems
Authors: Eric Jonas
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate on both synthetic and real data, showing that the resulting problem is invertible, is reasonably well-posed in the face of input data perturbations, and crucially performs well on experimentally-observed data for many molecules. For each spectrum in the test set our model generates 128 candidate structures and measure the fraction which are SMILES-correct. |
| Researcher Affiliation | Academia | Eric Jonas Department of Computer Science University of Chicago EMAIL |
| Pseudocode | No | The paper does not contain a dedicated 'Pseudocode' or 'Algorithm' section or figure. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | We obtain molecular structures consisting of H, C, O, and N atoms by downloading a large fraction of the public database Pub Chem []. ... We use NMRShift DB [18] for all data. |
| Dataset Splits | No | The paper states, 'This leaves us with 1,268,461 molecules, of which we train on 819,200 and draw the test set from the remainder.' It does not mention a separate validation set or specific validation split percentages/counts. |
| Hardware Specification | Yes | We train the network for 100 hours on an n Vidia v100 GPU. |
| Software Dependencies | No | The paper mentions software like 'Adam' (optimizer) and 'RDKit', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train using binary cross-entropy loss between the allowed possible next edges and the predicted edges of the network. We use Adam with a learning rate of 10 4 and train the network for 100 hours on an n Vidia v100 GPU. |