Research suggests that AI professionals looking for a new job prioritise flexibility and stability over exciting perks. Despite recent high-profile layoffs, the wider talent shortage is ongoing. Organisations looking to attract, or retain, the best candidates are offering numerous unique benefits. However, research from BenchSci finds that AI, machine learning, and data professionals are mostly looking for flexibility and stability in their future career. "While the global economy continues to face challenges and instability, competition for tech talent is not slowing down. This research, conducted with one of the most sought-after groups in terms of tech talent, clearly shows that well-recognised names and generous salaries are no longer enough to entice the brightest talent."

A $1000 CAD (or equivalent in your country's currency) work from home allowance to make your home setup perfect for you A lifestyle spending account for employees to receive reimbursement for eligible expenses related to wellness, lifestyle and productivity $2500 CAD (or equivalent in your country's currency) per year At BenchSci, we're committed to cultivating an inspiring, inclusive, and equitable work environment for high performing, ego-free, self-starting individuals with a growth mindset, who enjoy the challenge of solving hard problems. We recognize that everyone here is a person first and an employee second. We want people to feel cared for and supported to bring the best versions of themselves to work and help the company achieve its mission. We believe culture is critical to success and invest accordingly. We live and promote our FASTT values of Focused, Advancement with Speed, Tenacity, and Transparency. We work hard to maintain an engaging, supportive environment where everyone can do their best work. To learn more, read our culture deck.

A $1000 CAD (or equivalent in your country's currency) work from home allowance to make your home setup perfect for you A lifestyle spending account for employees to receive reimbursement for eligible expenses related to wellness, lifestyle and productivity $2500 CAD (or equivalent in your country's currency) per year At BenchSci, we're committed to cultivating an inspiring, inclusive, and equitable work environment for high performing, ego-free, self-starting individuals with a growth mindset, who enjoy the challenge of solving hard problems. We recognize that everyone here is a person first and an employee second. We want people to feel cared for and supported to bring the best versions of themselves to work and help the company achieve its mission. We believe culture is critical to success and invest accordingly. We live and promote our FASTT values of Focused, Advancement with Speed, Tenacity, and Transparency. We work hard to maintain an engaging, supportive environment where everyone can do their best work. To learn more, read our culture deck.

As more companies deploy machine learning for AI-enabled products and services, they face the challenge of carving out a defensible market position, especially if they are latecomers. The most successful AI users capture a good pool of training data early and then exploit feedback data to open up a value gap--in terms of prediction quality--between themselves and later movers. Latecomers can still secure a foothold if they can find sources of superior training data or feedback data, or if they tailor their predictions to a specific niche. The past decade has brought tremendous advances in an exciting dimension of artificial intelligence--machine learning. This technique for taking data inputs and turning them into predictions has enabled tech giants such as Amazon, Apple, Facebook, and Google to dramatically improve their products. It has also spurred start-ups to launch new products and platforms, sometimes even in competition with Big Tech.

As a machine learning researcher in the biology field, I have been keeping an eye on the recently emerging field of AI in drug discovery. Living in Toronto myself, where many "star" companies in this field were founded (Atomwise, BenchSci, Cyclica, Deep Genomics, ProteinQure… just to name a few!), I talked to many people in this field, and attended a few meetup events about this topic. What I learned is that this field is growing at such a rapid speed, and it is becoming increasing hard to keep track of all companies in this field and get a comprehensive view of them. Therefore, I decide to use my data science skills to track and analyze the companies in this field, and build an interactive dashboard (https://ai-drug-dash.herokuapp.com) to visualize some key insights from my analysis. The Chief Strategy Officer of BenchSci (one of the "star" AI-drug startups in Toronto), Simon Smith, is an excellent observer and communicator in the AI-drug discovery field.

The growing demand for ML/AI technologies, as well as for ML/AI talent, in the pharmaceutical industry is driving the formation of a new interdisciplinary field: data-driven drug discovery/healthcare. Consequently, there is a growing number of AI driven startups offering technology solutions for drug discovery/development. In drug development, preclinical phase (in vitro and in vivo), also named preclinical studies and nonclinical studies, is a stage of research that begins before clinical trials, and during which important feasibility, iterative testing and drug safety data are collected. According to a detailed mind-map prepared by Pharma Division of Deep Knowledge Analytics (updated Q1 2019): the AI for Drug Discovery, Biomarker Development and Advanced R&D Industry Landscape counts so far 400 investors, 170 companies and 50 corporations. This article focuses only on the AI startups and the AI investors trying to overcome the above 4 challenges during design and execution of the preclinical phase.

Clinical Informatics tells us that: "Every year in the U.S., approximately 2 million patients participate in roughly 3000 clinical trials; six million patients are needed to meet U.S. recruitment goals. Consequently, up to 90% of trials are delayed or over budget". Experts blame the lack of data available - to both patients and researchers - to explain why only 5% of cancer patients, for example, end up enrolling in clinical trials. A study from Carnegie Mellon University and Albert Ludwig University in Germany predicts that "AI could cut the cost of drug discovery by about 70%" and Krishna Yeshwant, general partner at Google Ventures, estimates "AI would cut (clinical trial) costs by 90 percent." Artificial intelligence seems like the perfect solution, but Zikria Syed writes in MedCityNews that "clinical trial technologies haven't changed much since the current categories -- clinical trial management systems, electronic data capture, and interactive voice response, -- were established in the late 1990s." A recent Deloitte study also that tells us "a number of clinical trial activities still use the same processes as in the 1990s." In a sector that is usually at the forefront of technology – biotechnology - it is hard to believe this is happening. I spoke to six innovators who were tackling the massive problem head on – scientists and entrepreneurs working to bring clinical trials to the people who need them – to find out what they are doing to solve the serious innovation problem. The list of people is impressive for the diversity of solutions they're offering to clinical trials: Anna Huyghues-Despointes, Head of Strategy, Owkin; Simon Smith, Chief Growth Officer, BenchSci; Leila Pirhaji, Founder & CEO, ReviveMed; Shai Shen-Orr, Founder, Cytoreason; and Daniel Jamieson, CEO Biorelate and Gunjan Bhardwaj, Founder & CEO, Innoplexus. Additionally we spoke to consultant Dr. Chrysanthi Ainali, Co-Founder Dignosis and Instructor for the KNect365 Learning Course AI & Real World Evidence for Clinical Trials to ask her thoughts on the specific challenges AI startups in clinical trials face. "Healthcare brings great challenges for a technology company. It is inherently conservative and risk averse - Hippocratic Oath: 'first, do no harm'" says Simon Smith, Chief Growth Officer at Benchsci.

BenchSci, an AI-powered search engine for biomedical researchers, has raised $8 million in a series A round of funding led by iNovia Capital, with participation from Google's recently announced Gradient Ventures, Golden Venture Partners, Afore Capital, Real Ventures, and Radical Ventures. Founded out of Toronto, Canada in 2015, BenchSci uses machine learning to translate both closed- and open-access data into recommendations for specific experiments that a researcher is planning to carry out. Ultimately, it's about speeding up studies to help biomedical professionals find reliable antibodies and reduce resource waste. "Without the use of AI, basic biomedical research is not only challenging, but drug discovery takes much longer and is more expensive," noted BenchSci cofounder and CEO Liran Belenzon. "We are applying and developing a number of advanced data science, bioinformatics, and machine learning algorithms to solve this problem and accelerate scientific discovery by ending reagent failure." Google's various associated investment funds, including Alphabet's GV, have invested in a number of biotech and life science startups in recent years, including next-gen vaccine discovery startup SpyBiotech.

The biomedical startup was founded by University of Toronto alumni David Q. Chen, Elvis Wianda, Liran Belenzon, Tom Leung.So far the venture has raised US$8 million, contributed by a group of investors including Montreal's iNovia Capital and Google's Gradient Ventures (which is Alphabet's AI venture capital firm). The new company is called BenchSci and it aims to use artificial intelligence to scan through millions of data points, drawn from published research papers, in order to find new compounds that can help to accelerate the drug discovery process. The focus of the new venture is with finding commercial antibodies. The researchers spent two years building machine learning software that can extract antibody usage data from published figures. This involves decoding millions of papers, with the end result of making the data easily discoverable for scientists.