artificial intelligence drugs

Artificial Intelligence for new drugs discovery

Biomedical science innovation based on AI technology is the long-awaited opportunity for achieving higher effectivity in this industry. New drug development through R&D innovation in a shorter time and at a lower cost is the Holy Grail of the biopharmaceutical industry.

Scientific innovation not only involves finding the molecular mechanism of a disease but also the development of new drugs for the cure, palliation or prevention of diseases.

Innovation in the pharmaceutical industry costs over 2.400,00 million euros, according to Farmaindustria. On the other hand, R&D global investment in the pharmaceutical sector accounts for 30.000,00 million, only in Europe and these figures hikes to 142.000,00 euros worldwide.

From this amount, 57% goes to design, development and clinical tests evaluation phases. The remaining 40% goes to basic research, approval processes and pharmacovigilance.

According to data provided by Biopharmaceutical representatives, developing a new medicine takes about 12 to 13 years from its discovery to its clinical use in patients. However, only a few molecules reach the commercialization phase. Many are left behind along the phases of the drug development process.

It´s precisely in drug targeting discovery and designs that AI-based techniques have cut downtime by half and costs by 25% in the production of new drugs.

Currently, the Spanish biopharmaceutical, Sylentis, has implemented a software based on Neural Networks, SVM and Machine Learning to gather, filter and reinterpret experimental data generated by the pharmaceutical industry. This allows them to enhance and develop the drugs thanks to a  software that trains to generate thousands of specific compounds to deal with a disease in a matter of a few days. The pharmaceutical company reduces the expensive and time- consuming task of candidate´s selection from years to only a few days.

AI for personalized drugs.

A survey conducted by Deloitte and MIT Sloan Management Review last June found out that only 20 % of biopharmaceutical companies are digitally mature enough, and the lack of a clear vision, leadership and financing are slowing down companies´ growth.

According to MarketsandMarkets, AI´s demand in the biopharmaceutical industry is expected to grow from US$ 198.3 million in 2018 to US$ 3.88 billion in 2025.

The four projected areas to drive most of the AI market forward in biopharmacy between 2018 and 2025: drugs discovery, precision medicine, diagnostic imaging and medical diagnosis and research. The report says drugs´ discovery reached a larger market share during the survey period.

These areas that go from the target candidate molecules selection to the production of the new drug provides a unique opportunity to speed up drugs development. The potential improvement of the process includes:

  • Process redesign to speed up new molecules discovery time and is based on expert knowledge.
  • Digitalization of repetitive processes automation and generation of new content and data.
  • Advanced analytics incorporating internal and external sources. Here new predictive model would be included.

A key role for AI algorithms is molecules interaction forecasting to find the disease mechanisms. In turn, these mechanisms could help setting new biomarkers to identify, design, validate and optimize new drugs candidate target and identify existing drugs that could be reused for other indications

 

28 November, 2019 Blog Comments Off on Artificial Intelligence for new drugs discovery
data translator

The hidden figure behind a successful AI implementation in the organizations.

The Artificial Intelligence implementation in companies is cross functional: Marketing, Finance, Operations… they all have benefited from the emergence of data driven across business processes in their organizations.

In a recent study published by Fujitsu and Pier Audoin Consultants, shows that the benefits companies have gained through Artificial Intelligence implementation are starting to pay off. This is not a matter of five years´ time. The AI´ s time has come. However, the figures are still low: only 11% of the surveyed companies are implementing AI strategies, 29% have AI projects in progress and 35% expect to do it in the next two years.

Under this classification, they would be defined as innovators, early adopters, followers. In other words, based on the company´s maturity and data adaptation, they will belong to one of the groups before mentioned.

Accordingly, 53% of the companies that have implemented AI or have in mind doing it believe improvement of automation processes depends on it, whereas almost 75% are creating business units for AI´s implementation take-off. The main areas this technology is implemented on is higher production efficiency, maintenance forecasting and above all, in customers´ behavior forecasting for appropriate business actions.

Nevertheless, a survey delivered by MIT Sloan Management Review and Boston Consulting Group a few weeks ago highlighted different data.

Although it claims AI´s rewards promise, these are not risk-free for example, a competitor taking the risk and going a step ahead. These are the innovators that use AI for the company´s across business processes alignment, investment and integration.

Many leading companies see AI not as an opportunity but as a risk strategy.  And this perception has gone up from 37% to 45% from 2017 to 2019 respectively.

Concerning risk management, many AI based initiatives have failed. Seven out of ten of the surveyed companies claimed they have hardly benefited from this technology. And it´s not a trivial matter when almost 90% of companies have invested in AI.

Thus, even if some companies have found out success with AI, most struggle to add value based on it. As a result, many executives face challenges associated with AI: It´s a source of non- exploited opportunities, an inherent risk. But, above all, it´s an urgent issue to tackle. How can executives exploit the opportunities, manage risks and minimize AI associated problems?

Data translator: the hidden figure

Professionals training, not only in technical and scientific areas but also in communication and interpretation, becomes essential for the differential AI value generation. Deep understanding of the business needs and knowing how to convey that to the technical teams in charge of implementing AI is the Holy Grail of all the companies and providers of this service.

On the other hand, Mckinsey says that success results based on AI and data analytics do not depend only on data scientists, data engineers or data analytics teams. A transversal figure is required: a data translator.

Mckinsey believes this figure can ensure the organizations achieve real impact from their analytical initiatives as it can help to understand correctly the business needs and translate them into a scientific -technical language and vice versa.

Data translation experience allows this figure to get deep knowledge of the core business and its value chain in diverse areas: distribution, health, marketing, manufacturing or any other environment.

As the consulting company defines it, in their role, translators help to guarantee deep knowledge generated through sophisticated analytics is translated into impact at every level of the organization. By 2026, The Global McKinsey Institute estimates translators demand will reach two or four million only in the U.S.

Thus, translators take advantage of their insights in AI and analytics to convey these commercial objectives to data professionals who will create the models and the solutions. Finally, the translators ensure the solution produce the insights the company can interpret and execute and ultimately, communicates the benefits of these insights to the businessmen to boost adoption.

One way to reduce the risk strategy companies have taken when they decide to be ahead of their competitors in their sector, is without any doubt, the capacity to interpret the data and offer insights based on them.

 

 

11 November, 2019 Blog Comments Off on The hidden figure behind a successful AI implementation in the organizations.
marketing ia

The top ten AI uses in Marketing [Infographics].

Nowadays Artificial Intelligence based applications in marketing and sales enable companies to know their customers better and to offer them the best products promotions in real time. Chief Marketing Officers-CMO and their teams need automatic learning and artificial intelligence to stand out and take advantage over their competitors.

In pursuit of customer satisfaction, the best CMOs manage to balance their marketing strategies and elements that make the company brand and experience unique.

Knowing how potential buyers make up their minds on how, when and where to buy, turn marketing strategies more interesting. Advanced analytics enables customers segmentation for better knowledge of their preferences. Thanks to this knowledge, products purchasing propensity or churn prevention in the purchasing process or suitable pricing setting can be estimated, among many other things.

According to a recent survey delivered by Forbes Insights and Quantcast Research, the use of AI allows marketing and sales departments to boost sales by 52% and increase customer retention by 49%.

The infographics shows data of the ten most relevant Artificial Intelligence contributions to marketing teams. In the next two years, according to the reports, the implementation of Artificial Intelligence based technologies and automatic learning will be adopted by the companies that realize their benefits.

 

AI-marketing

21 August, 2019 Blog Comments Off on The top ten AI uses in Marketing [Infographics].
concierto-musica

Math and music: Advantages of using artificial intelligence

We are used to getting Spotify´s music selection and classification based on what we have listened to and our music taste. Thus, the Swedish company must upload over 20000 new songs or podcasts each day and thanks to artificial intelligence´s help. It provides the music we have listened to the most for some time and its function: “your summer memories” and it creates different music groups depending on what we have listened to lately.

With this help, music gender classification has become obsolete as music lists generation by artificial intelligence do not depend on music gender but on “the good music”. It´s obvious not everybody likes the same music gender, however, specific mathematical patterns which are transversal to music gender do work. Consequently, there are people who like Pop music and still enjoy a song classified as Rock music.

There are different fields in which artificial intelligence can improve music processes. In the 50s, Alan Turing was the first to record computer-generated music. This was the start of an interesting area in which AI-created music through reinforced learning. The algorithm learned what characteristics and patterns created a specific music gender and finally composed.

Thanks to this application, artificial intelligence helps companies to create new music or assist composers in their creations.

Another field in which artificial intelligence has great acceptance is editing. The experience of listening to music with a clear and clean sound is, without any doubt, one of the main features music lovers appreciate the most. Although the creative component is still necessary, AI can train to edit their audios properly to those not having that creative skill

When we talk about applied mathematics to music, we must understand what is involved in diverse areas such as tuning, musical notes, chords, harmonies, rhythm, beat, and nomenclature.

The beginning

In 2002, Polyphonic HMI was founded on the premises of using artificial intelligence and apply it to the music industry. Based on the study of the mathematical components of music, the probabilities of success of a song could be determined. Although an artist´s success depends on many factors, this system helped to simplify the task of finding which song could serve as a launch single of a new album and even of a new artist. Thanks to this, record companies, producers and representatives could allocate resources in a more favorable context. Music commercialization has always been an expensive business and a big challenge to finding promising artists and successful songs.

Fifteen years later, the leading technology companies are investing in this technology focusing on different processes of the music industry. Thanks to mathematics, we can see the impact of artificial intelligence on the music we listen to which enrich our musical experience.

 

12 August, 2019 Blog Comments Off on Math and music: Advantages of using artificial intelligence
fraude

Supervised and unsupervised learning techniques for fraud detection.

Artificial intelligence is redefining fraud prevention techniques as it allows to obtain information based on experience. This information consists of transaction activities, behavior and trends. Before the use of artificial intelligence, the applied methods were based on rules that helped to analyze historical fraud patterns but could not prevent them. Although these models could identify fraud attempts, they did not provide information of the future.

Nevertheless, the technological sophistication of fraud crimes is higher and more precise and efficient attacks have grown during the last few years

As a result, leading companies dealing with potential fraud crimes, mainly in banking and insurance, must increase monitoring accuracy and acuity of customers potential risk for the institution. Decision-making on accepting or rejecting payments, limiting charges refund and reducing operational and reputational risks is much easier now.

Fraud prevention in the future will depend on a combination of supervised and unsupervised automatic learning techniques. Supervised automatic learning finds patters based on historical events, factors, trends, etc., and unsupervised automatic learning looks for relationships and variable links, a combination of both methodologies would help to prevent fraud in the following:

Detection in real time. The use of artificial intelligence enables the detection of attacks in real time, instead of weeks that usually takes to start receiving reverse requests of charges.

Thwarting the most sophisticated attempts of fraud. Fraud techniques get more and more sophisticated. Artificial intelligence would help to prevent and reduce these attacks.

Scoring in real time. Provide analysts a scoring for a better perspective to set the limits to maximize sales and minimize losses in real time.

Immediate transactions. Fraud prevention systems based on AI enable immediate transaction´s approval provided it is within the reverse charges threshold of the main debit and credit cards.

Reduction of false positives. False positives are reduced thanks to supervised and unsupervised automatic learning, whereas current techniques cannot efficiently detect them. Frequently, when a customer pays an unusual amount of money or from a new location, the card is blocked by the system as it interprets it wrongly as a suspicious activity. With artificial intelligence, it is possible to identify more precisely any change in customers expenditure habits.

Profitability in low margin products. AI has allowed insurance companies to continue their profitable business and attract new customers whose historical purchases are not part of the historical supervised learning of fraud systems

Supervised and unsupervised learning should be complemented with experts´ knowledge aiming at a mixed approach to focus their attention in more suspicious cases detected by AI.

31 July, 2019 Blog Comments Off on Supervised and unsupervised learning techniques for fraud detection.

Health, the new challenge in Artificial Intelligence

Data has significantly grown with the advent of network devices in the health sector such as medical clinical histories, diagnostic processes, and more particularly, medical imaging -the introduction of Real-World Evidence. Making correct use of this data could save a great number of lives and reduce sanitary costs.

27 June, 2018 Blog Comments Off on Health, the new challenge in Artificial Intelligence

SISCLAP

SISCLAP Proyecto Salud

 

The SISCLAP project’s objective is to obtain an SCP, which achieves capitation payment models, adjusted for the risk level of the beneficiaries of a health service. Thus it is intended to improve the efficiency of resources, reducing use and consumption and the differences between budgets and expenditure. As an added value seeks to improve the access of users of increased risk to the most efficient and necessary.

The SISCLAP project is basically based on the application of algorithms and predictive models (using artificial intelligence) for segmenting all patients operated on supercomputing infrastructure with a focus on improving the efficiency of the Spanish health system. This improvement will be through three related levels, which ordered from micro to macro: Professional Management, Strategic Management and Organization and Financing of health centres, and budget allocation of healthcare providers.

 

The “Ministerio de Industria, Energía y Turismo” of Spain, within the “Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica 2008-2011”, has funded the project SISCLAP.

 

SISCLAP consortium has been formed by:

  • FlowLab
  • Baladona Serveis Assistencials
  • Fundación Parque Científico de Murcia

 

 

Reference number : TSI-020100-2011-193

1 January, 2010 Proyectos No comments yet