To determine the cost necessary to provide the desired service, we have a clear baseline. Our benchmark takes into account a number of crucial factors, including whether or not the requested service requires the outsourcing of a particular technology from a foreign country to ensure the quality of the data results, whether technical consultation with the research team is necessary, and whether the work is simply routine. Additionally, the price will differ if the requested service includes interpreted data reports in its package. The service of creating machine learning models requires the most consultation with the research team, multiple in-person meetings with the service requestor, and extensive work from the computation and drug discovery/development research teams to achieve the requestor’s desired outcome. In order to find the most effective forecasting models, one of Drug Discovery Pro’s important services, machine learning, demands financial support from both the company and the service requestor.

Supporting graduate, master’s, and doctoral students financially so they can pursue their studies in drug discovery and development is one of the charitable endeavors of the Drug Discovery Pro. The current discount is 50% for students and 10% to 50% for group applications for online courses. The Goldstar alumni who completed courses with Drug Discovery Pro and acquired the Goldstar tag on their certificates are eligible for discounts of between 10% and 20% on upcoming courses. In most cases, the researcher receives a 10% discount for any requested service if another researcher refers them to make a purchase. A full exemption is also possible for the researcher if 10 service requestors use his or her name as a recommendation.

• Website: You click the contact link on the home page, in the message box, enter the required information, type your message inside the box, and then click Send.
• E-mail: admin@drugdiscoverypro.com

Yes. The computing team at Drug Discovery Pro, which has experts in computational chemistry, machine learning programming, algorithm development, and model optimization, can work on any required compound database for a particular protein target. They prepare the data, create the model, test it, refine it as necessary, and ultimately produce the model for the target bioactivity prediction.