RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design

• URL: http://arxiv.org/abs/2011.13042v1
• Date: Wed, 25 Nov 2020 22:04:16 GMT
• Title: RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
• Authors: Cheng-Hao Liu, Maksym Korablyov, Stanis{\l}aw Jastrz\k{e}bski, Pawe{\l} W{\l}odarczyk-Pruszy\'nski, Yoshua Bengio, Marwin H. S. Segler
• Abstract summary: We train deep graph neural networks to approximate the outputs of a retrosynthesis planning software. Our approach finds molecules predicted to be more likely to be antibiotics while maintaining good drug-like properties and being easily synthesizable.
• Score: 75.14290780116002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility. However, using currently available proxies still results in highly unrealistic compounds. We investigate the feasibility of training deep graph neural networks to approximate the outputs of a retrosynthesis planning software, and their use to bias the search process. We evaluate our method on a benchmark involving searching for drug-like molecules with antibiotic properties. Compared to enumerating over five million existing molecules from the ZINC database, our approach finds molecules predicted to be more likely to be antibiotics while maintaining good drug-like properties and being easily synthesizable. Importantly, our deep neural network can successfully filter out hard to synthesize molecules while achieving a $10^5$ times speed-up over using the retrosynthesis planning software.

Related papers

• SDDBench: A Benchmark for Synthesizable Drug Design [31.739548311094843]
  We propose a new, data-driven metric to evaluate molecule synthesizability. Our approach directly assesses the feasibility of synthetic routes for a given molecule through our proposed round-trip score. To demonstrate the efficacy of our method, we conduct a comprehensive evaluation of round-trip scores alongside search success rate across a range of representative molecule generative models.
  arXiv  Detail & Related papers  (2024-11-13T03:08:33Z)
• RGFN: Synthesizable Molecular Generation Using GFlowNets [51.33672611338754]
  We propose Reaction-GFlowNet, an extension of the GFlowNet framework that operates directly in the space of chemical reactions. RGFN allows out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.
  arXiv  Detail & Related papers  (2024-06-01T13:11:11Z)
• Generative Active Learning for the Search of Small-molecule Protein Binders [87.106635995213]
  We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small soluble molecules that inhibit the enzyme Epoxide Hydrolase 2 (sEH) LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH.
  arXiv  Detail & Related papers  (2024-05-02T16:39:21Z)
• STRIDE: Structure-guided Generation for Inverse Design of Molecules [0.24578723416255752]
  $textbfSTRIDE$ is a generative molecule workflow that generates novel molecules with an unconditional generative model guided by known molecules without any retraining. Our generated molecules have on average 21.7% lower synthetic accessibility scores and also reduce ionization potential by 5.9% of generated molecules via guiding.
  arXiv  Detail & Related papers  (2023-11-06T08:22:35Z)
• Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks [0.0]
  We propose an end-to-end generative system, DrugGEN, for the de novo design of drug candidate molecules. The system is trained using a large dataset of drug-like compounds and target-specific bioactive molecules. Using the open-access DrugGEN, it is possible to easily train models for other druggable proteins.
  arXiv  Detail & Related papers  (2023-02-15T18:59:27Z)
• Exploring Chemical Space with Score-based Out-of-distribution Generation [57.15855198512551]
  We propose a score-based diffusion scheme that incorporates out-of-distribution control in the generative differential equation (SDE) Since some novel molecules may not meet the basic requirements of real-world drugs, MOOD performs conditional generation by utilizing the gradients from a property predictor. We experimentally validate that MOOD is able to explore the chemical space beyond the training distribution, generating molecules that outscore ones found with existing methods, and even the top 0.01% of the original training pool.
  arXiv  Detail & Related papers  (2022-06-06T06:17:11Z)
• Barking up the right tree: an approach to search over molecule synthesis DAGs [28.13323960125482]
  Current deep generative models for molecules ignore synthesizability. We propose a deep generative model that better represents the real world process. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules.
  arXiv  Detail & Related papers  (2020-12-21T17:35:06Z)
• RetroXpert: Decompose Retrosynthesis Prediction like a Chemist [60.463900712314754]
  We devise a novel template-free algorithm for automatic retrosynthetic expansion. Our method disassembles retrosynthesis into two steps. While outperforming the state-of-the-art baselines, our model also provides chemically reasonable interpretation.
  arXiv  Detail & Related papers  (2020-11-04T04:35:34Z)
• ChemoVerse: Manifold traversal of latent spaces for novel molecule discovery [0.7742297876120561]
  It is essential to identify molecular structures with the desired chemical properties. Recent advances in generative models using neural networks and machine learning are being widely used to design virtual libraries of drug-like compounds.
  arXiv  Detail & Related papers  (2020-09-29T12:11:40Z)
• Retrosynthesis Prediction with Conditional Graph Logic Network [118.70437805407728]
  Computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. We propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model built upon graph neural networks.
  arXiv  Detail & Related papers  (2020-01-06T05:36:57Z)

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.