I’m currently building Future Therapeutics. It is a TechBio company based in Berlin where we're developing and leveraging a proprietary, data-independent AI infrastructure to discover life-saving treatments. The problem with current AI approaches to drug discovery is their need for large training datasets which are costly and time-consuming to generate through wet lab experiments.At Future Therapeutics, we're trying to solve this problem by developing a Specialized AI Operating System that requires minimal to no experimentally validated training data. Currently, our focus is on small-molecule chemistry. The idea is straightforward: if we can bypass the data requirement, which is essentially the major bottleneck in realizing the full potential of AI for drug discovery, we can significantly accelerate drug discovery campaigns.We've already developed and experimentally validated the foundational technology, yielding promising results. With this technology, we're enabling drug candidate discovery campaigns that don't rely on existing experimental data, such as known small molecules and/or the 3D structure of the target. For us to initiate a drug candidate identification project, all we require is the sequence of the target.As we continue to build our technology for end-to-end preclinical campaigns, we are actively pursuing collaborations with industry and academic partners for drug candidate identification and optimization projects. Feel free to reach out to discuss your project.If you're a skilled professional with expertise in quantum physics, reinforcement learning, molecular AI, or a commercial background, and you're eager to engage in exciting projects, don't hesitate to reach out. We'd be delighted to discuss potential working opportunities with you.Keywords for my profile: deep learning, machine learning, artificial intelligence drug discovery, machine learning drug discovery, reinforcement learning drug discovery, generative adversarial networks, GAN, cheminformatics, computational drug discovery