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  1. AI-Augmented Drug Discovery Creative Biolabs provides innovative drug discovery services based on our original Artificial Intelligence-augmented technology, especially for the discovery of therapeutic antibodies and small molecules. Email: Address: SUITE 203, 17 Ramsey Road, Shirley, NY 11967, USA Web:
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  4. Introducing a new drug to market can cost pharmaceutical companies an average $2.6 billion and 11-15 years of research and development. Even once new drug candidates show potential in laboratory testing, less than 10% of drug candidates make it to market following Phase I trials. Between 2010 and 2017, 76% of new drugs approved by the US Food and Drug Administration (FDA) are small molecules. $2.6 B 10% 76% WHY USE AI IN DRUG DISCOVERY?
  5. After making it through the preclinical development phase, and receiving approval from the FDA, researchers begin testing the drug with human participants. AI can facilitate participant monitoring during clinical trials—generating a larger set of data more quickly—and aid in participant retention by personalizing the trial experience. AI in Clinical Trials (Phase III) The drug discovery process ranges from reading and analyzing already existing literature, to testing the ways potential drugs interact with targets. According to report, AI could curb drug discovery costs for companies by as much as 70%. AI in Drug Discovery (Phase I) The preclinical development phase of drug discovery involves testing potential drug targets on animal models. Utilizing AI during this phase could help trials run smoothly and enable researchers to more quickly and successfully predict how a drug might interact with the animal model. AI in Preclinical Development (Phase II)
  6. Ø Predicting 3D structure of target protein Ø Predicting drug-protein interactions Ø AI in determining drug activity Ø AI in de novo drug design AI in drug design AI In Drug Discovery AI in polypharmacology Ø Designing biospecific drug molecules Ø Designing multitarget drug molecules AI in chemical synthesis Ø Predicting reaction yield Ø Predicting retrosynthesis pathways Ø Developing insights into reaction mechanisms Ø Designing synthetic route AI in drug repurposing Ø Identification of therapeutic target Ø Prediction of new therapeutic use AI in drug screening Ø Prediction of toxicity Ø Prediction of bioactivity Ø Prediction of physicochemical property Ø Identification and classification of target cells
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  8. Classes of Learning Tasks and Techniques Mix of supervised and unsupervised learning, where less expensive and more abundant unlabeled data can be utilized to train a classifier. Semisupervised Learning (Fig. A) A learning algorithm can interactively query the user to determine labels for unlabeled data in the regions of the input space about which the model is least certain. Active Learning (Fig. B) Describes a family of algorithms that relax the common assumption that the training and test data should be in the same feature space and follow the same distribution. Transfer Learning (Fig. D) Can be treated as a geometric or topological problem, the goal is to find similarities and differences between data points used to spatially order data. Unsupervised Learning The goal is to reconstruct the unknown function f that assigns output values y to data points x. Supervised Learning Instead of learning only one task at a time, as in single-task learning, several different but conceptually related tasks are learned in parallel and make use of a shared internal representation. Multitask Learning (Fig. E) To some extent strives to emulate reward-driven learning, and in its simplest configuration, an agent attempts to find the optimal set of actions to promote some outcome. Reinforcement Learning (Fig. C) Xin Y,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119 (18): 10520-10594.
  9. Bayesian methods are those that explicitly apply Bayes’ theorem to classification and regression problems. Bayesian Algorithms It is called instance-based because it builds the hypotheses from the training instances. It is also known as memory-based learning or lazy-learning. Instance-Based Methods Algorithms for constructing decision trees usually work top-down, by choosing a variable at each step that best splits the set of items. Decision Tree Algorithms In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Ensemble Algorithms Dimensionality reduction seeks a lower- dimensional representation of numerical input data that preserves the salient relationships in the data. Dimensionality Reduction Artificial neural networks (ANNs) consist of input, hidden, and output layers with connected neurons (nodes) to simulate the human brain. Artificial Neural Networks Common Learning Algorithms
  10. Bayesian Algorithms Liu ZH,et al. ChemStable: A web server for rule-embedded naïve Bayesian learning approach to predict compound stability. J. Comput. Aided Mol. Des. 2014, 28: 941-950.
  11. Instance-Based Methods SVM is a supervised machine learning algorithm used for both classification and regression. The objective of SVM algorithm is to find a hyperplane in an N-dimensional space that distinctly classifies the data points. Support Vector Machine A SOM or self-organizing feature map is an unsupervised machine learning technique used to produce a low-dimensional representation of a higher dimensional data set while preserving the topological structure of the data. Self-organizing Map KNN is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. K-nearest Neighbor Xin Y,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119 (18): 10520-10594.
  12. Decision Tree Algorithms Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. Random Forest A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. Decision Tree Xin Y,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119 (18): 10520-10594.
  13. Ensemble Algorithms Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. In boosting, a random sample of data is selected, fitted with a model and then trained sequentially—that is, each model tries to compensate for the weaknesses of its predecessor. Boosting Bagging, is the ensemble learning method that is commonly used to reduce variance within a noisy dataset. In bagging, a random sample of data in a training set is selected with replacement— meaning that the individual data points can be chosen more than once. Bagging Xin Y,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119 (18): 10520-10594.
  14. Dimensionality Reduction LDA is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. Linear Discriminant Analysis Image From Wikipedia A visual depiction of the resulting PCA projection for a set of 2D points. A visual depiction of the resulting LDA projection for a set of 2D points. PCA is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Principal Component Analysis
  15. Artificial Neural Networks DNN refers to an ANN that has several hidden layers with several differences. Deep nets process data in complex ways by employing sophisticated math modeling. Deep Neural Networks ANNs are computing systems inspired by the biological neural networks that constitute animal brains. A typical ANN architecture contains many artificial neurons arranged in a series of layers: the input layer, an output layer, i.e., the top layer, which generates a desired prediction ( ADMET properties, activity, a vector of fingerprint etc.), and one or more hidden layer where the intermediate representations of the input data are transformed. Artificial neural networks Image From Wikipedia
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  17. DeepVS: Boosting Docking-Based Virtual Screening with DL Pereira J.C. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model. 2016;56:2495–2506. Mostafa K. DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics. 2019, 35(18):3329–3338. The deep neural network that is introduced, DeepVS, uses the output of a docking program and learns how to extract relevant features from basic data. The approach introduces the use of atom and amino acid embeddings and implements an effective way of creating distributed vector representations of protein–ligand complexes by modeling the compound as a set of atom contexts that is further processed by a convolutional layer. DeepVS
  18. DeepAffinity: DL Method Used to Measure DTBA Mostafa K. DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics. 2019, 35(18):3329–3338. DeepAffinity is a deep learning methods used to measure drug target binding affinity. Under novel representations of structurally-annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for joint ly encoding molecular representations and predicting affinities. Performances for new protein classes with few labeled data are further improved by transfer learning. DeepAffinity
  19. DeepTox: Toxicity Prediction Using Deep Learning Mayr A. DeepTox: toxicity prediction using deep learning. Front. Environ. Sci. 2016, 3:80. Representation of a toxicophore by hierarchically related features.
  20. AI-Based QSAR Models Image From Wikipedia Profile-QSAR SVM QSAR Bayesian QSAR Multitask QSAR
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  22. • High throughput, screen large numbers of clones • Large library capacity: from 107 to over 108 • Various phage display systems (M13,λ,T7) • Tailored biopanning strategies • Wide range of applications Antibody Production by Phage Display Creative Biolabs has combined AI, big data, machine learning, and phage display to generate a novel AI-powered computational antibody drug discovery platform. Aided by this innovative platform, one-stop human antibody discovery services are provided, including antibody-antigen binding prediction, antibody candidate generation, antibody sequence optimization, and antibody production & characterization. AI-Based One-stop Antibody Discovery Platform • Discover and analyze new antibody clusters • Generate new sequences within existing clusters • Accelerate the generation of high-affinity antibodies • Rapidly generate novel antibody sequences using computational algorithms to help improve affinity, solubility, manufacturability, specificity, and stability Augmented Antibody Discovery with Al
  23. AI can typically generate 10 times more antibody sequence clusters than a laboratory- based approach alone. Diversity leads to the discovery of new binding modalities and potentially new therapeutic modes-of-action. Antibody Discovery Services Creative Biolabs is specialized in designing and performing high-quality custom AI-based antibody screening assays, with different formats, endpoints, parameters, to satisfy any specific requirement. Antibody Screening Services Creative Biolabs offers a wide variety of antibody engineering services to quickly and efficiently optimize the existing antibodies via AI based algorithms, such as affinity, solubility, cross-reactivity, manufacturability, immunogenicity, specificity, and stability. Creative Biolabs has applied AI technology in small molecule design and optimization to promote its affinity, specificity, and validity. Our innovative AI methods range from in silico molecule screening, molecular modeling, to AI-based molecule optimization. Small Molecule Design & Optimization Creative Biolabs provides the best strategy and customized protocols for model training data service, and ultimately, to accelerate the novel candidate drug discovery. AI-Augmented Drug Discovery at Creative Biolabs Antibody Engineering Services Model Training Data Services
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