Lingo3DMol: Generation of a Pocket-based 3D Molecule using a Language Model

Structure-based drug design powered by deep generative models have attracted increasing research interest in recent years. Language models have demonstrated a robust capacity for generating valid molecules in 2D structures, while methods based on geometric deep learning can directly produce molecules with accurate 3D coordinates. Inspired by both methods, this article proposes a pocket-based 3D molecule generation method that leverages the language model with the ability to generate 3D coordinates. High quality protein-ligand complex data are insufficient; hence, a perturbation and restoration pre-training task is designed that can utilize vast amounts of small-molecule data. A new molecular representation, a fragment-based SMILES with local and global coordinates, is also presented, enabling the language model to learn molecular topological structures and spatial position information effectively. Ultimately, CrossDocked and DUD-E dataset is employed for evaluation and additional metrics are introduced. This method achieves state-of-the-art performance in nearly all metrics, notably in terms of binding patterns, drug-like properties, rational conformations, and inference speed. Our model is available as an online service to academic users via sw3dmg.stonewise.c

Prevention and treatment of renal osteodystrophy in children on chronic renal failure: European guidelines

Childhood renal osteodystrophy (ROD) is the consequence of disturbances of the calcium-regulating hormones vitamin D and parathyroid hormone (PTH) as well as of the somatotroph hormone axis associated with local modulation of bone and growth cartilage function. The resulting growth retardation and the potentially rapid onset of ROD in children are different from ROD in adults. The biochemical changes of ROD as well as its prevention and treatment affect calcium and phosphorus homeostasis and are directly associated with the development of cardiovascular disease in pediatric renal patients. The aims of the clinical and biochemical surveillance of pediatric patients with CRF or on dialysis are prevention of hyperphosphatemia, avoidance of hypercalcemia and keeping the calcium phosphorus product below 5 mmol(2)/l(2). The PTH levels should be within the normal range in chronic renal failure (CRF) and up to 2–3 times the upper limit of normal levels in dialysed children. Prevention of ROD is expected to result in improved growth and less vascular calcification

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits

A Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design

In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Different ESP prediction ML models were proposed to generate surface molecular charge distribution. Electrostatic complementarity (EC) can quantitatively apply ESP data to scale the complementarity between a ligand and its binding pocket, leading to the potential to increase efficiency of drug design. However, there is not much research discussing EC score functions and its application boundary. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirmed its effectiveness against the available Pearson’s R correlation coefficient. Additionally, the application boundary of the EC score and two indices used to define the EC score application scope will be discussed

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000–2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon

The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380–1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of “5 × 1” and “3 × 1” kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis

Code for Lingo3DMol

Lingo3DMol is a pocket-based 3D molecule generation method that combines the ability of language model with the ability to generate 3D coordinates and geometric deep learning to produce high-quality molecules.System RequirementsHardware requirementsA standard computer with GPU with at least 5GB graphic memory.OS RequirementsThis package is supported for macOS and Linux. The package has been tested on the following systems: Linux: Ubuntu 16.04 macOS: Ventura (13.0)Install via conda yaml fileTypical install time: 40 minutesconda create -n lingo3dmol python=3.8conda activate lingo3dmolconda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forgepip install scipy==1.7.3 pandas==1.5.1 numpy==1.20.3 rdkit==2022.09.1DatasetsWe provide DUD-E pocket files for sampling under \dataset folder. Please Unzipdude_pocket.zip.Model CheckpointsDownload and move these checkpoint to the \checkpoint folder.https://stonewise-lingo3dmol-public.s3.cn-northwest-1.amazonaws.com.cn/contact.pkl md5sum:6a9313726141fcf9201b9b9470dc2a7ehttps://stonewise-lingo3dmol-public.s3.cn-northwest-1.amazonaws.com.cn/gen_mol.pkl md5sum:452bd401667184ae43c9818e5bdb133bSamplingTo inference using the model on DUD-E set, run this code:sh run.shExpected outputThe output should be generated molecules in mol format.Main Parameters Help--coc_dis Define the collision distance with the pocket (A)--nci_thrs Define the threshold of the nci prediction model--topk Select top k nci--max_run_hours Define the max run hours--gennums Define the minimum generation numbers--USE_THRESHOLD Define sampled only categories larger than expectation--isMultiSample Define sampled use multinomial--isGuideSample frag-based sampled use this definition--OnceMolGen no frag-based sample--gen_frag_set Define the number of collections generated, from which the top twenty percent is selected (batch size)--prod_time "go_factory()" func repeat time--tempture "proj1" used Tsoftmax--frag_len_add Defines the minimum length of each generated fragment/ bigger fragments faster generatedLicenseThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.</p