RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro

• URL: http://arxiv.org/abs/2202.04202v1
• Date: Mon, 7 Feb 2022 02:54:29 GMT
• Title: RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
• Authors: Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martinez-Pena, Eileen L. Tang, Suraj M S, Cristian Regep, Jeremy Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, and Yoshua Bengio
• Abstract summary: We employ a sequential model optimization search applied to a deep learning model to quickly discover highly synergistic drug combinations active against a cancer cell line. We find that the set of combinations queried by our model is enriched for highly synergistic combinations. Remarkably, we rediscovered a synergistic drug combination that was later confirmed to be under study within clinical trials.
• Score: 46.773794687622825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Selecting optimal drug repurposing combinations for further preclinical development is a challenging technical feat. Due to the toxicity of many therapeutic agents (e.g., chemotherapy), practitioners have favoured selection of synergistic compounds whereby lower doses can be used whilst maintaining high efficacy. For a fixed small molecule library, an exhaustive combinatorial chemical screen becomes infeasible to perform for academic and industry laboratories alike. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are highly biased towards synergistic agents and these results do not necessarily generalise out of distribution. We employ a sequential model optimization search applied to a deep learning model to quickly discover highly synergistic drug combinations active against a cancer cell line, while requiring substantially less screening than an exhaustive evaluation. Through iteratively adapting the model to newly acquired data, after only 3 rounds of ML-guided experimentation (including a calibration round), we find that the set of combinations queried by our model is enriched for highly synergistic combinations. Remarkably, we rediscovered a synergistic drug combination that was later confirmed to be under study within clinical trials.

Related papers

• Retrosynthesis prediction enhanced by in-silico reaction data augmentation [66.5643280109899]
  We present RetroWISE, a framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation. On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models.
  arXiv  Detail & Related papers  (2024-01-31T07:40:37Z)
• SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design [64.69434941796904]
  We propose a novel setting and models for in-context drug synergy learning. We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context.
  arXiv  Detail & Related papers  (2023-06-19T17:03:46Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations [0.9854322576538699]
  computational screening has become an important way to prioritize drug combinations. DeepDDS was superior to competitive methods by more than 16% predictive precision.
  arXiv  Detail & Related papers  (2021-07-06T08:25:43Z)
• Neural networks for Anatomical Therapeutic Chemical (ATC) [83.73971067918333]
  We propose combining multiple multi-label classifiers trained on distinct sets of features, including sets extracted from a Bidirectional Long Short-Term Memory Network (BiLSTM) Experiments demonstrate the power of this approach, which is shown to outperform the best methods reported in the literature.
  arXiv  Detail & Related papers  (2021-01-22T19:49:47Z)
• CASTELO: Clustered Atom Subtypes aidEd Lead Optimization -- a combined machine learning and molecular modeling method [2.8381402107366034]
  We propose a combined machine learning and molecular modeling approach that automates lead optimization workflow. Our method provides new hints for drug modification hotspots which can be used to improve drug efficacy.
  arXiv  Detail & Related papers  (2020-11-27T15:41:00Z)
• Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models [38.637412590671865]
  We propose a emphbiological bottleneck model that jointly learns drug-target interaction and synergy. The model consists of two parts: a drug-target interaction and target-disease association module. We experimentally tested the model predictions in the U.S. National Center for Advancing Translational Sciences.
  arXiv  Detail & Related papers  (2020-11-09T03:30:44Z)
• Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints [84.09488581365484]
  Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds becomes more complex. Most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability.
  arXiv  Detail & Related papers  (2020-06-09T03:06:45Z)
• Prediction of Drug Synergy by Ensemble Learning [0.0]
  We investigate the effectiveness of different compound representations in predicting the drug synergy. On a large drug combination screen dataset, we first demonstrate the use of a promising representation that has not been used for this problem before. We then propose an ensemble on representation-model combinations that outperform each of the baseline models.
  arXiv  Detail & Related papers  (2020-01-07T12:21:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.