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Automatic retrosynthetic route planning using template-free models.

ABSTRACT: Retrosynthetic route planning can be considered a rule-based reasoning procedure. The possibilities for each transformation are generated based on collected reaction rules, and then potential reaction routes are recommended by various optimization algorithms. Although there has been much progress in computer-assisted retrosynthetic route planning and reaction prediction, fully data-driven automatic retrosynthetic route planning remains challenging. Here we present a template-free approach that is independent of reaction templates, rules, or atom mapping, to implement automatic retrosynthetic route planning. We treated each reaction prediction task as a data-driven sequence-to-sequence problem using the multi-head attention-based Transformer architecture, which has demonstrated power in machine translation tasks. Using reactions from the United States patent literature, our end-to-end models naturally incorporate the global chemical environments of molecules and achieve remarkable performance in top-1 predictive accuracy (63.0%, with the reaction class provided) and top-1 molecular validity (99.6%) in one-step retrosynthetic tasks. Inspired by the success rate of the one-step reaction prediction, we further carried out iterative, multi-step retrosynthetic route planning for four case products, which was successful. We then constructed an automatic data-driven end-to-end retrosynthetic route planning system (AutoSynRoute) using Monte Carlo tree search with a heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The end-to-end model for reaction task prediction can be easily extended to larger or customer-requested reaction databases. Our study presents an important step in realizing automatic retrosynthetic route planning.

SUBMITTER: Lin K

PROVIDER: S-EPMC8152431 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionBoth Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming.To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR-based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases.MethodsWe performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning.Results(1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1 mm(proximal femur 0.2 ± 0.1 mm and acetabulum 0.3 ± 0.5 mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent.(3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion.ConclusionsAutomatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D-models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.

Project description:PURPOSE:The purpose of this study was to develop an auto-planning platform to be interfaced with a commercial treatment planning system (TPS). The main goal was to obtain robust and high-quality plans for different anatomic sites and various dosimetric requirements. METHODS:Monaco (Elekta, St. Louis, US) was the TPS in this work. All input parameters for inverse planning could be defined in a plan template inside Monaco. A software tool called Robot Framework was used to launch auto-planning trials with updated plan templates. The template modifier external to Monaco was the major component of our auto-planning platform. For current implementation, it was a rule-based system that mimics the trial-and-error process of an experienced planner. A template was automatically updated by changing the optimization constraints based on dosimetric evaluation of the plan obtained in the previous trial, along with the data of the iterative optimization extracted from Monaco. Treatment plans generated by Monaco with all plan evaluation criteria satisfied were considered acceptable, and such plans would be saved for further evaluation by clinicians. The auto-planning platform was validated for 10 prostate and 10 head-and-neck cases in comparison with clinical plans generated by experienced planners. RESULTS:The performance and robustness of our auto-planning platform was tested with clinical cases of prostate and head and neck treatment. For prostate cases, automatically generated plans had very similar plan quality with the clinical plans, and the bladder volume receiving 62.5 Gy, 50 Gy, and 40 Gy in auto-plans was reduced by 1%, 3%, and 5%, respectively. For head and neck cases, auto-plans had better conformity with reduced dose to the normal structures but slightly higher dose inhomogeneity in the target volume. Remarkably, the maximum dose in the spinal cord and brain stem was reduced by more than 3.5 Gy in auto-plans. Fluence map optimization only with less than 30 trials was adequate to generate acceptable plans, and subsequent optimization for final plans was completed by Monaco without further intervention. The plan quality was weakly dependent on the parameter selection in the initial template and the choices of the step sizes for changing the constraint values. CONCLUSION:An automated planning platform to interface with Monaco was developed, and our reported tests showed preliminary results for prostate and head and neck cases.

Project description:An automatic brain-metastases planning (ABMP) software has been installed in our institution. It is dedicated for treating multiple brain metastases with radiosurgery on linear accelerators (linacs) using a single-setup isocenter with noncoplanar dynamic conformal arcs. This study is to validate the calculated absolute dose and dose distribution of ABMP. Three types of measurements were performed to validate the planning software: 1, dual micro ion chambers were used with an acrylic phantom to measure the absolute dose; 2, a 3D cylindrical phantom with dual diode array was used to evaluate 2D dose distribution and point dose for smaller targets; and 3, a 3D pseudo-in vivo patient-specific phantom filled with polymer gels was used to evaluate the accuracy of 3D dose distribution and radia-tion delivery. Micro chamber measurement of two targets (volumes of 1.2 cc and 0.9 cc, respectively) showed that the percentage differences of the absolute dose at both targets were less than 1%. Averaged GI passing rate of five different plans measured with the diode array phantom was above 98%, using criteria of 3% dose difference, 1 mm distance to agreement (DTA), and 10% low-dose threshold. 3D gel phantom measurement results demonstrated a 3D displacement of nine targets of 0.7 ± 0.4 mm (range 0.2 ~ 1.1 mm). The averaged two-dimensional (2D) GI passing rate for several region of interests (ROI) on axial slices that encompass each one of the nine targets was above 98% (5% dose difference, 2 mm DTA, and 10% low-dose threshold). Measured D95, the minimum dose that covers 95% of the target volume, of the nine targets was 0.7% less than the calculated D95. Three different types of dosimetric verification methods were used and proved the dose calculation of the new automatic brain metastases planning (ABMP) software was clinical acceptable. The 3D pseudo-in vivo patient-specific gel phantom test also served as an end-to-end test for validating not only the dose calculation, but the treatment delivery accuracy as well.

Project description:Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented.The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other.18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved.The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.

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Automatic retrosynthetic route planning using template-free models.

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