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Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain.

ABSTRACT: Computer Assisted Synthesis Planning (CASP) has gained considerable interest as of late. Herein we investigate a template-based retrosynthetic planning tool, trained on a variety of datasets consisting of up to 17.5 million reactions. We demonstrate that models trained on datasets such as internal Electronic Laboratory Notebooks (ELN), and the publicly available United States Patent Office (USPTO) extracts, are sufficient for the prediction of full synthetic routes to compounds of interest in medicinal chemistry. As such we have assessed the models on 1731 compounds from 41 virtual libraries for which experimental results were known. Furthermore, we show that accuracy is a misleading metric for assessment of the policy network, and propose that the number of successfully applied templates, in conjunction with the overall ability to generate full synthetic routes be examined instead. To this end we found that the specificity of the templates comes at the cost of generalizability, and overall model performance. This is supplemented by a comparison of the underlying datasets and their corresponding models.

SUBMITTER: Thakkar A

PROVIDER: S-EPMC7012039 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain.

Thakkar Amol A Kogej Thierry T Reymond Jean-Louis JL Engkvist Ola O Bjerrum Esben Jannik EJ

Chemical science 20191105 1

Computer Assisted Synthesis Planning (CASP) has gained considerable interest as of late. Herein we investigate a template-based retrosynthetic planning tool, trained on a variety of datasets consisting of up to 17.5 million reactions. We demonstrate that models trained on datasets such as internal Electronic Laboratory Notebooks (ELN), and the publicly available United States Patent Office (USPTO) extracts, are sufficient for the prediction of full synthetic routes to compounds of interest in me ...[more]

PMID: 32110367

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Project description:INTRODUCTION:Facial transplantation represents one of the most complicated scenarios in craniofacial surgery because of skeletal, aesthetic, and dental discrepancies between donor and recipient. However, standard off-the-shelf vendor computer-assisted surgery systems may not provide custom features to mitigate the increased complexity of this particular procedure. We propose to develop a computer-assisted surgery solution customized for preoperative planning, intraoperative navigation including cutting guides, and dynamic, instantaneous feedback of cephalometric measurements/angles as needed for facial transplantation and other related craniomaxillofacial procedures. METHODS:We developed the Computer-Assisted Planning and Execution (CAPE) workstation to assist with planning and execution of facial transplantation. Preoperative maxillofacial computed tomography (CT) scans were obtained on 4 size-mismatched miniature swine encompassing 2 live face-jaw-teeth transplants. The system was tested in a laboratory setting using plastic models of mismatched swine, after which the system was used in 2 live swine transplants. Postoperative CT imaging was obtained and compared with the preoperative plan and intraoperative measures from the CAPE workstation for both transplants. RESULTS:Plastic model tests familiarized the team with the CAPE workstation and identified several defects in the workflow. Live swine surgeries demonstrated utility of the CAPE system in the operating room, showing submillimeter registration error of 0.6 ± 0.24 mm and promising qualitative comparisons between intraoperative data and postoperative CT imaging. CONCLUSIONS:The initial development of the CAPE workstation demonstrated that integration of computer planning and intraoperative navigation for facial transplantation are possible with submillimeter accuracy. This approach can potentially improve preoperative planning, allowing ideal donor-recipient matching despite significant size mismatch, and accurate surgical execution for numerous types of craniofacial and orthognathic surgical procedures.

Project description:Objective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using "spatial priors" derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices. Methods: Thirty-two patients underwent consecutive SEEG implantations utilizing computer-assisted planning over 2 years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbor (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically. Results: All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements. Conclusions: We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed.

Project description:Laser interstitial thermal therapy (LITT) is an alternative to open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Studies suggest maximal ablation of the mesial hippocampal head and amygdalohippocampal complex (AHC) improves seizure freedom rates while better neuropsychological outcomes are associated with sparing of the parahippocampal gyrus (PHG). Optimal trajectories avoid sulci and CSF cavities and maximize distance from vasculature. Computer-assisted planning (CAP) improves these metrics, but the combination of entry and target zones has yet to be determined to maximize ablation of the AHC while sparing the PHG. We apply a machine learning approach to predict entry and target parameters and utilize these for CAP. Ten patients with hippocampal sclerosis were identified from a prospectively managed database. CAP LITT trajectories were generated using entry regions that include the inferior occipital, middle occipital, inferior temporal, and middle temporal gyri. Target points were varied by sequential AHC erosions and transformations of the centroid of the amygdala. A total of 7600 trajectories were generated, and ablation volumes of the AHC and PHG were calculated. Two machine learning approaches (random forest and linear regression) were investigated to predict composite ablation scores and determine entry and target point combinations that maximize ablation of the AHC while sparing the PHG. Random forest and linear regression predictions had a high correlation with the calculated values in the test set (ρ = 0.7) for both methods. Maximal composite ablation scores were associated with entry points around the junction of the inferior occipital, middle occipital, and middle temporal gyri. The optimal target point was the anteromesial amygdala. These parameters were then used with CAP to generate clinically feasible trajectories that optimize safety metrics. Machine learning techniques accurately predict composite ablation score. Prospective studies are required to determine if this improves seizure-free outcome while reducing neuropsychological morbidity following LITT for MTLE.

Project description:BackgroundSafety planning is a brief intervention that has become an accepted practice in many clinical settings to help prevent suicide. Even though it is quick compared to other approaches, it frequently requires 20 min or more to complete, which can impede adoption. A self-administered, Web-based safety planning application could potentially reduce clinician time, help promote standardization and quality, and provide enhanced ability to share the created plan.ObjectiveThe aim of this study was to design, build, and test the usability of a Web-based, self-administered safety planning application.MethodsWe employed a user-centered software design strategy led by a multidisciplinary team. The application was tested for usability with a target sample of suicidal patients. Detailed observations, structured usability ratings, and Think Aloud procedures were used. Suicidal ideation intensity and perceived ability to cope were assessed pre-post engagement with the Web application.ResultsA total of 30 participants were enrolled. Usability ratings were generally strong, and all patients successfully built a safety plan. However, the completeness of the safety plan varied. The mean number of steps completed was 5.5 (SD 0.9) out of 6, with 90% (27/30) of participants completing at least 5 steps and 67% (20/30) completing all 6 steps. Some safety planning steps were viewed as inapplicable to some individuals. Some confusion in instructions led to modifications to improve understandability of each step. Ratings of suicide intensity after completion of the application were significantly lower than preratings, pre: mean 5.11 (SD 2.9) versus post: mean 4.46 (SD 3.0), t27=2.49, P=.02. Ratings of ability to cope with suicidal thoughts after completion of the application were higher than preratings, with the difference approaching statistical significance, pre: mean 5.93 (SD 2.9), post: mean 6.64 (SD 2.4), t27=-2.03, P=.05.ConclusionsWe have taken the first step toward identifying the components needed to maximize usability of a self-administered, Web-based safety planning application. Results support initial consideration of the application as an adjunct to clinical contact. This allows for the clinician or other personnel to provide clarification, when needed, to help the patient build the plan, and to help review and revise the draft.

Dataset Information

Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain.

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Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain.

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