Machine Learning
Machine Learning
Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that enable computer systems to learn from data without being explicitly programmed. In other words, it involves the use of computational methods to automatically discover patterns in data and make predictions or decisions based on those patterns. Machine learning has become increasingly important in recent years due to the explosion of digital data and the need for automated decision-making in various industries, such as finance, healthcare, and marketing.
There are three main types of machine learning:
- Supervised learning,
- unsupervised learning
- Reinforcement learning.
Supervised Learning
Involves training a model on labeled data, where each example is associated with a target output. The goal is to learn a mapping between inputs and outputs that can be used to make predictions on new, unseen data.
Unsupervised Learning
On the other hand, involves training a model on unlabeled data with no specific target output. The goal is to discover patterns or structures. One of the most cutting-edge technologies now available is the application of machine learning to drug discovery. The difficulty with this method of drug discovery is that it requires data science in addition to drug discovery research. The data science and machine learning experts at Drug Discovery Pro can assist in gathering the most recent information on newly registered medications that target a variety of hot targets and create a solid database for virtual screening. The scientists working at Drug Discovery Pro are quite accomplished; they have over 20 years of machine learning experience, and they are able to provide accurate regression from the training set of compounds for the virtual screening of the testing set of compounds. It is important to note that while machine learning is not appropriate for all projects, it can be used when accurate data regarding the training set and the protein target are available. Drug research Professional specialists can adaptably fill in the missing data as needed and release the regression model prior to the virtual screening, at the client’s request. Additionally, Drug Discovery Pro offers both qualitative and quantitative predictions of a molecule’s bioactivity.
Reinforcement Learning
Reinforcement learning is a subfield of machine learning concerned with how intelligent agents should behave in a given environment in order to maximize the concept of cumulative reward. Reinforcement learning, along with supervised and unsupervised learning, is one of three fundamental machine learning paradigms.
Reinforcement learning differs from supervised learning in that it does not need the presence of labeled input/output pairings or the explicit correction of suboptimal behaviors. The emphasis instead is on striking a balance between exploration (of the undiscovered region) and utilization (of existing information). Because many reinforcement learning algorithms for this context use dynamic programming approaches, the environment is generally represented in the form of a Markov decision process (MDP). The primary distinction between classical dynamic programming methods and reinforcement learning algorithms is that the latter does not require knowledge of an exact mathematical model of the MDP and is designed to target big MDPs where accurate approaches become infeasible.
Reinforcement learning differs from supervised learning in that it does not need the presence of labeled input/output pairings or the explicit correction of suboptimal behaviors. The emphasis instead is on striking a balance between exploration (of the undiscovered region) and utilization (of existing information). Because many reinforcement learning algorithms for this context use dynamic programming approaches, the environment is generally represented in the form of a Markov decision process (MDP). The primary distinction between classical dynamic programming methods and reinforcement learning algorithms is that the latter does not require knowledge of an exact mathematical model of the MDP and is designed to target big MDPs where accurate approaches become infeasible.
The available machine-learning models for predicting chemical bioactivity are drawn from the databases of the medicinal classes displayed in the mini cards below.
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EP2,4 receptor antagonists
310EGP Add to cart -
Phosphodiesterase 5 (PDE5) inhibitors
465EGP Add to cart -
BACE2 inhibitors
310EGP Add to cart -
BTK inhibitors
310EGP Add to cart -
CDK9 inhibitors
620EGP Add to cart -
Bromodomain containing protein 4 (BRD4) inhibitors
620EGP Add to cart -
Chemokine receptor (CC) antagonists
465EGP Add to cart -
Toll-like receptor (TLR7,8) agonists
620EGP Add to cart -
Histone Deacetylase 6 (HDAC6) inhibitors
310EGP Add to cart
Attention!
- The models have an accuracy of 85-95%.
- The pricing given on each model is for predicting ONLY one compound’s bioactivity.
- Based on the nanomolar potency, the models are designed to predict chemical bioactivity. If the chemical properties of the test molecule do not match the model’s feature scheme, or if the machine predicts micromolar potency, the compound will be inactive.
- The compound’s smiles code is used for data entry to forecast activity.
- After the compound(s) prediction fees are submitted to drug discovery pro, the requestor should proceed to the service request button to complete the appropriate information and upload a pdf file with the smiles code(s) of the test compound(s). The results are then forwarded immediately to the requestor via the email address provided.
