| 1. |
- Alvarsson, Jonathan, et al.
(författare)
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Ligand-Based Target Prediction with Signature Fingerprints
- 2014
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:10, s. 2647-2653
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Tidskriftsartikel (refereegranskat)abstract
- When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.
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| 2. |
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| 3. |
- Ahlberg, Ernst, et al.
(författare)
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Using conformal prediction to prioritize compound synthesis in drug discovery
- 2017
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Ingår i: Proceedings of Machine Learning Research. - Stockholm : Machine Learning Research. ; , s. 174-184
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Konferensbidrag (refereegranskat)abstract
- The choice of how much money and resources to spend to understand certain problems is of high interest in many areas. This work illustrates how computational models can be more tightly coupled with experiments to generate decision data at lower cost without reducing the quality of the decision. Several different strategies are explored to illustrate the trade off between lowering costs and quality in decisions.AUC is used as a performance metric and the number of objects that can be learnt from is constrained. Some of the strategies described reach AUC values over 0.9 and outperforms strategies that are more random. The strategies that use conformal predictor p-values show varying results, although some are top performing.The application studied is taken from the drug discovery process. In the early stages of this process compounds, that potentially could become marketed drugs, are being routinely tested in experimental assays to understand the distribution and interactions in humans.
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| 4. |
- Atance, Sara Romeo, et al.
(författare)
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De Novo Drug Design Using Reinforcement Learning with Graph- Based Deep Generative Models
- 2022
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 62:20, s. 4863-4872
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning provides effective computational tools for exploring the chemical space via deep generative models. Here, we propose a new reinforcement learning scheme to finetune graph-based deep generative models for de novo molecular design tasks. We show how our computational framework can successfully guide a pretrained generative model toward the generation of molecules with a specific property profile, even when such molecules are not present in the training set and unlikely to be generated by the pretrained model. We explored the following tasks: generating molecules of decreasing/increasing size, increasing drug-likeness, and increasing bioactivity. Using the proposed approach, we achieve a model which generates diverse compounds with predicted DRD2 activity for 95% of sampled molecules, outperforming previously reported methods on this metric.
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| 5. |
- Bender, Andreas, et al.
(författare)
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Evaluation guidelines for machine learning tools in the chemical sciences
- 2022
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Ingår i: Nature Reviews Chemistry. - : Springer Science and Business Media LLC. - 2397-3358. ; 6:6, s. 428-442
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generation, improvement and/or ordering of research hypotheses. Despite the overarching applicability of ML workflows, one usually finds diverse evaluation study designs. The current heterogeneity in evaluation techniques and metrics leads to difficulty in (or the impossibility of) comparing and assessing the relevance of new algorithms. Ultimately, this may delay the digitalization of chemistry at scale and confuse method developers, experimentalists, reviewers and journal editors. In this Perspective, we critically discuss a set of method development and evaluation guidelines for different types of ML-based publications, emphasizing supervised learning. We provide a diverse collection of examples from various authors and disciplines in chemistry. While taking into account varying accessibility across research groups, our recommendations focus on reporting completeness and standardizing comparisons between tools. We aim to further contribute to improved ML transparency and credibility by suggesting a checklist of retro-/prospective tests and dissecting their importance. We envisage that the wide adoption and continuous update of best practices will encourage an informed use of ML on real-world problems related to the chemical sciences. [Figure not available: see fulltext.]
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| 6. |
- Bonner, Stephen, et al.
(författare)
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A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
- 2022
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; In Press
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Forskningsöversikt (refereegranskat)abstract
- Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.
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| 7. |
- Bonner, Stephen, et al.
(författare)
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Implications of topological imbalance for representation learning on biomedical knowledge graphs
- 2022
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.
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| 8. |
- Bost, Jeremy P., et al.
(författare)
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Novel endosomolytic compounds enable highly potent delivery of antisense oligonucleotides
- 2022
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- The therapeutic and research potentials of oligonucleotides (ONs) have been hampered in part by their inability to effectively escape endosomal compartments to reach their cytosolic and nuclear targets. Splice-switching ONs (SSOs) can be used with endosomolytic small molecule compounds to increase functional delivery. So far, development of these compounds has been hindered by a lack of high-resolution methods that can correlate SSO trafficking with SSO activity. Here we present in-depth characterization of two novel endosomolytic compounds by using a combination of microscopic and functional assays with high spatiotemporal resolution. This system allows the visualization of SSO trafficking, evaluation of endosomal membrane rupture, and quantitates SSO functional activity on a protein level in the presence of endosomolytic compounds. We confirm that the leakage of SSO into the cytosol occurs in parallel with the physical engorgement of LAMP1-positive late endosomes and lysosomes. We conclude that the new compounds interfere with SSO trafficking to the LAMP1-positive endosomal compartments while inducing endosomal membrane rupture and concurrent ON escape into the cytosol. The efficacy of these compounds advocates their use as novel, potent, and quick-acting transfection reagents for antisense ONs.
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| 9. |
- Buendia, Ruben, et al.
(författare)
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Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors
- 2019
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 59:3, s. 1230-1237
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Tidskriftsartikel (refereegranskat)abstract
- Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn - ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.
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| 10. |
- Buonfiglio, Rosa, et al.
(författare)
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Investigating Pharmacological Similarity by Charting Chemical Space
- 2015
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 55:11, s. 2375-2390
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Tidskriftsartikel (refereegranskat)abstract
- In this study, biologically relevant areas of the chemical space were analyzed using ChemGPS-NP. This application enables comparing groups of ligands within a multidimensional space based on principle components derived from physicochemical descriptors. Also, 3D visualization of the ChemGPS-NP global map can be used to conveniently evaluate bioactive compound similarity and visually distinguish between different types or groups of compounds. To further establish ChemGPS-NP as a method to accurately represent the chemical space, a comparison with structure-based fingerprint has been performed. Interesting complementarities between the two descriptions of molecules were observed. It has been shown that the accuracy of describing molecules with physicochemical descriptors like in ChemGPS-NP is similar to the accuracy of structural fingerprints in retrieving bioactive molecules. Lastly, pharmacological similarity of structurally diverse compounds has been investigated in ChemGPS-NP space. These results further strengthen the case of using ChemGPS-NP as a tool to explore and visualize chemical space.
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