| 1. |
- Johansson, Simon, 1994, et al.
(författare)
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AI-assisted synthesis prediction
- 2019
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Ingår i: Drug Discovery Today: Technologies. - : Elsevier BV. - 1740-6749. ; 32-33:December, s. 65-72
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Tidskriftsartikel (refereegranskat)abstract
- Application of AI technologies in synthesis prediction has developed very rapidly in recent years. We attempt here to give a comprehensive summary on the latest advancement on retro-synthesis planning, forward synthesis prediction as well as quantum chemistry-based reaction prediction models. Besides an introduction on the AI/ML models for addressing various synthesis related problems, the sources of the reaction datasets used in model building is also covered. In addition to the predictive models, the robotics based high throughput experimentation technology will be another crucial factor for conducting synthesis in an automated fashion. Some state-of-the-art of high throughput experimentation practices carried out in the pharmaceutical industry are highlighted in this chapter to give the reader a sense of how future chemistry will be conducted to make compounds faster and cheaper. © 2020 Elsevier Ltd
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| 2. |
- Johansson, Simon, 1994, et al.
(författare)
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De novo generated combinatorial library design
- 2024
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Ingår i: Digital Discovery. - 2635-098X. ; 3:1, s. 122-135
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence (AI) contributes new methods for designing compounds in drug discovery, ranging from de novo design models suggesting new molecular structures or optimizing existing leads to predictive models evaluating their toxicological properties. However, a limiting factor for the effectiveness of AI methods in drug discovery is the lack of access to high-quality data sets leading to a focus on approaches optimizing data generation. Combinatorial library design is a popular approach for bioactivity testing as a large number of molecules can be synthesized from a limited number of building blocks. We propose a framework for designing combinatorial libraries using a molecular generative model to generate building blocks de novo, followed by using k-determinantal point processes and Gibbs sampling to optimize a selection from the generated blocks. We explore optimization of biological activity, Quantitative Estimate of Drug-likeness (QED) and diversity and the trade-offs between them, both in single-objective and in multi-objective library design settings. Using retrosynthesis models to estimate building block availability, the proposed framework is able to explore the prospective benefit from expanding a stock of available building blocks by synthesis or by purchasing the preferred building blocks before designing a library. In simulation experiments with building block collections from all available commercial vendors near-optimal libraries could be found without synthesis of additional building blocks; in other simulation experiments we showed that even one synthesis step to increase the number of available building blocks could improve library designs when starting with an in-house building block collection of reasonable size.
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| 3. |
- Johansson, Simon, 1994, et al.
(författare)
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Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ; , s. 5260-5265
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Konferensbidrag (refereegranskat)abstract
- Determinantal point processes (DPPs) have become prominent in data summarization and recommender system tasks for their ability to simultaneously model diversity as well as relevance. In practical applications, k-Determinantal point processes (k-DPPs) are used to yield a selection of k items from a set of size N that are the most representative of the set. In this paper, we study a special case of the diverse subset selection problem where a fixed set GO is already given as a forced recommendation and the task is to determine the remainder of the recommendation G1. The standard k-DPP optimization objectives here can suggest items that are close to optimal when considering only items in G1, but are arbitrarily close to items in G0, i.e., they might not be sufficiently diverse w.r.t. G0. We explore a semi-supervised k-DPP objective that simultaneously considers G0 and G1 and compares the difference between the two recommendations. We demonstrate our findings using multiple examples where the diverse subset selection problem with forced recommendation is important in practice.
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| 4. |
- Johansson, Simon, 1994
(författare)
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Intelligent data acquisition for drug design through combinatorial library design
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A problem that occurs in machine learning methods for drug discovery is a need for standardized data. Methods and interest exist for producing new data but due to material and budget constraints it is desirable that each iteration of producing data is as efficient as possible. In this thesis, we present two papers methods detailing different problems for selecting data to produce. We invest- igate Active Learning for models that use the margin in model decisiveness to measure the model uncertainty to guide data acquisition. We demonstrate that the models perform better with Active Learning than with random acquisition of data independent of machine learning model and starting knowledge. We also study the multi-objective optimization problem of combinatorial library design. Here we present a framework that could process the output of gener- ative models for molecular design and give an optimized library design. The results show that the framework successfully optimizes a library based on molecule availability, for which the framework also attempts to identify using retrosynthesis prediction. We conclude that the next step in intelligent data acquisition is to combine the two methods and create a library design model that use the information of previous libraries to guide subsequent designs.
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| 5. |
- Johansson, Simon, 1994, et al.
(författare)
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Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction
- 2022
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Ingår i: Molecular Informatics. - : Wiley. - 1868-1743 .- 1868-1751. ; 41:12
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Tidskriftsartikel (refereegranskat)abstract
- Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit how much data can be obtained from experiments. This suggests the use of schemes for data collection such as active learning, which identifies the data points of highest impact for model accuracy, and which has been used in recent studies with success. However, little has been done to explore the robustness of the methods predicting reaction yield when used together with active learning to reduce the amount of experimental data needed for training. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined AUROC faster than random sampling on both datasets. Analysis of feature importance of the trained machine learning models suggests active learning had a larger influence on the model accuracy when only a few features were important for the model prediction.
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| 6. |
- Mervin, Lewis H., et al.
(författare)
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Uncertainty quantification in drug design
- 2021
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Ingår i: Drug Discovery Today. - : Elsevier BV. - 1878-5832 .- 1359-6446. ; 26:2, s. 474-489
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Forskningsöversikt (refereegranskat)abstract
- Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design–make–test–analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.
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| 7. |
- Shevtsov, Oleksii, 1988, et al.
(författare)
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A de novo molecular generation method using latent vector based generative adversarial network
- 2019
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946 .- 1758-2946. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: One to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases. Sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.[Figure not available: See fulltext.]
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| 8. |
- Thakkar, Amol, et al.
(författare)
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Artificial intelligence and automation in computer aided synthesis planning
- 2021
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Ingår i: Reaction Chemistry and Engineering. - : Royal Society of Chemistry (RSC). - 2058-9883. ; 6:1, s. 27-51
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Forskningsöversikt (refereegranskat)abstract
- In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years. We first answer the question: what is computer assisted synthesis planning (CASP) and why is it relevant to drug discovery and development? We draw a distinction between discovery and development, focusing on their differing requirements. We highlight the need for an automated synthesis platform which chemists can use to augment their workflows and what it entails. The interaction between experimental and computational scientists is emphasized as a key driver in the development of such technologies. Advances in the development and application of algorithms is then covered, drawing a distinction between physics based and statistical or data driven modelling paradigms, their use in, and how they contribute to augmented drug discovery and development. Finally, developments in the coupling of artificial intelligence and automation are discussed. Throughout, we emphasize the need for an inter-disciplinary approach, blurring the distinction between fields in the pursuit of artificial intelligence and automated platforms that can be integrated into chemical workflows.
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